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Ingrid Van Keilegom

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2019. "Estimation of the Boundary of a Variable Observed with A Symmetric Error," TSE Working Papers 19-990, Toulouse School of Economics (TSE).

    Cited by:

    1. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    2. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    3. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    4. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    5. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    6. Simar, Léopold & Wilson, Paul, 2021. "Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs," LIDAM Discussion Papers ISBA 2021003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    8. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2024. "Penalized sieve estimation of zero‐inefficiency stochastic frontiers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 41-65, January.

  2. Jeffrey Racine & Ingrid Van Keilegom, 2017. "A Smooth Nonparametric, Multivariate, Mixed-Data Location-Scale Test," Department of Economics Working Papers 2017-13, McMaster University.

    Cited by:

    1. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

  3. BIRKE, Mélanie & VAN BELLEGEM, Sébastien & VAN KEILEGOM, Ingrid, 2016. "Semi-Parametric Estimation in a Single- Index Model with Endogenous Variables," LIDAM Discussion Papers CORE 2016022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Xin Geng & Carlos Martins-Filho & Feng Yao, 2015. "Estimation of a Partially Linear Regression in Triangular Systems," Working Papers 15-46, Department of Economics, West Virginia University.
    2. Zhang, Hong-Fan, 2021. "Iterative GMM for partially linear single-index models with partly endogenous regressors," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    3. Alan P. Ker & Abdoul G. Sam, 2018. "Semiparametric estimation of the link function in binary-choice single-index models," Computational Statistics, Springer, vol. 33(3), pages 1429-1455, September.
    4. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.

  4. Van Keilegom, Ingrid & Vanhems, Anne, 2016. "Estimation of a semiparametric transformation model in the presence of endogeneity," TSE Working Papers 16-654, Toulouse School of Economics (TSE).

    Cited by:

    1. Kloodt, Nick, 2021. "Identification in a fully nonparametric transformation model with heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 170(C).
    2. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    3. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    4. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
    5. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.

  5. Jean-Pierre Florens & Joel L. Horowitz & Ingred van Keilegom, 2016. "Bias-corrected confidence intervals in a class of linear inverse problems," CeMMAP working papers CWP19/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.

  6. Leopold Simar & Ingrid Van Keilegom & Valentin Zelenyuk, 2014. "Nonparametric Least Squares Methods for Stochastic Frontier Models," CEPA Working Papers Series WP032014, School of Economics, University of Queensland, Australia.

    Cited by:

    1. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    2. Abdelaati Daouia & Jean-Pierre Florens & Léopold Simar, 2020. "Robust frontier estimation from noisy data: a Tikhonov regularization approach," Post-Print hal-02573853, HAL.
    3. Parmeter, Christopher F. & Simar, Léopold & Van Keilegom, Ingrid & Zelenyuk, Valentin, 2021. "Inference in the Nonparametric Stochastic Frontier Model," LIDAM Discussion Papers ISBA 2021029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances II," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 9, pages 371-408, Springer.
    5. Xu Guo & Gao-Rong Li & Michael McAleer & Wing-Keung Wong, 2017. "Specification Testing of Production in a Stochastic Frontier Model," Tinbergen Institute Discussion Papers 17-097/III, Tinbergen Institute.
    6. Paul W. Wilson & Shirong Zhao, 2022. "Evidence from shadow price of equity on “Too-Big-to-Fail” Banks," Journal of Productivity Analysis, Springer, vol. 57(1), pages 23-40, February.
    7. José Luis Preciado Arreola & Daisuke Yagi & Andrew L. Johnson, 2020. "Insights from machine learning for evaluating production function estimators on manufacturing survey data," Journal of Productivity Analysis, Springer, vol. 53(2), pages 181-225, April.
    8. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    9. Parmeter, Christopher F., 2021. "Is it MOLS or COLS?," Efficiency Series Papers 2021/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    10. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2021. "Density deconvolution with Laplace errors and unknown variance," Journal of Productivity Analysis, Springer, vol. 56(2), pages 103-113, December.
    11. Valentin Zelenyuk & Valentyn Panchenko, 2023. "Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP022023, School of Economics, University of Queensland, Australia.
    12. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2020. "Exploring Hospital Efficiency within and between Italian Regions: New Empirical Evidence," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202024, University of Turin.
    13. Worthington, Andrew C. & Zelenyuk, Valentin, 2018. "Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese bankingAuthor-Name: Du, Kai," European Journal of Operational Research, Elsevier, vol. 265(2), pages 748-764.
    14. Artem Prokhorov & Kien C. Tran & Mike G. Tsionas, 2021. "Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors," Empirical Economics, Springer, vol. 60(6), pages 3043-3068, June.
    15. Zhou, Jianhua & Parmeter, Christopher F. & Kumbhakar, Subal C., 2020. "Nonparametric estimation of the determinants of inefficiency in the presence of firm heterogeneity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1142-1152.
    16. Valentin Zelenyuk & Zhichao Wang, 2023. "Random vs. Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals," CEPA Working Papers Series WP052023, School of Economics, University of Queensland, Australia.
    17. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    18. Christopher F. Parmeter & Shirong Zhao, 2023. "An alternative corrected ordinary least squares estimator for the stochastic frontier model," Empirical Economics, Springer, vol. 64(6), pages 2831-2857, June.
    19. Nickolaos G. Tzeremes, 2019. "Technological change, technological catch-up and export orientation: evidence from Latin American Countries," Journal of Productivity Analysis, Springer, vol. 52(1), pages 85-100, December.
    20. Tadesse Getacher Engida & Christopher F. Parmeter & Xudong Rao & Alfons G.J.M. Oude Lansink, 2022. "Investment Inefficiency and Corporate Social Responsibility," Journal of Productivity Analysis, Springer, vol. 58(1), pages 95-108, August.
    21. Mike Tsionas & Valentin Zelenyuk, 2021. "Goodness-of-fit in Optimizing Models of Production: A Generalization with a Bayesian Perspective," CEPA Working Papers Series WP182021, School of Economics, University of Queensland, Australia.
    22. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    23. Giovanni Forchini & Raoul Theler, 2023. "Semi-parametric modelling of inefficiencies in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 59(2), pages 135-152, April.
    24. Ahmed S & Sonia Pérez-F & Carlos Carleos A & Norberto C & Pablo Martínez C, 2018. "Inference in Stochastic Frontier Models Based on Asymmetry," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(4), pages 99-108, January.
    25. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.
    26. Levent Kutlu & Shasha Liu & Robin C. Sickles, 2022. "Cost, Revenue, and Profit Function Estimates," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 16, pages 641-679, Springer.
    27. Zeebari, Zangin & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2021. "Regularized Conditional Estimators of Unit Inefficiency in Stochastic Frontier Analysis, with Application to Electricity Distribution Market," Ratio Working Papers 345, The Ratio Institute.
    28. Simar, Leopold & Wilson, Paul, 2015. "Statistical Approaches for Nonparametric Frontier Models: A Guided Tour," LIDAM Reprints ISBA 2015022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    29. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
    30. Simar, Léopold & Wilson, Paul, 2021. "Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs," LIDAM Discussion Papers ISBA 2021003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    31. Paul W. Wilson, 2022. "Turbulent Years for U.S. Banks: 2000-20," Review, Federal Reserve Bank of St. Louis, vol. 104(3), pages 189-209, July.
    32. Khodadadipour, M. & Hadi-Vencheh, A. & Behzadi, M.H. & Rostamy-malkhalifeh, M., 2021. "Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 613-628.
    33. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    34. Xu Guo & Tao Wang & Lixing Zhu, 2016. "Model checking for parametric single-index models: a dimension reduction model-adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1013-1035, November.
    35. Cinzia Daraio & Leopold Simar, 2022. "Approximations and Inference for Nonparametric Production Frontiers," LEM Papers Series 2022/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    36. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    37. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    38. Christopher F. Parmeter & Valentin Zelenyuk, 2016. "A Bridge Too Far? The State of the Art in Combining the Virtues of Stochastic Frontier Analysis and Data Envelopement Analysis," Working Papers 2016-10, University of Miami, Department of Economics.
    39. Guo, Xu & Li, Gao Rong & Wong, Wing Keung, 2014. "Specification Testing of Production Frontier Function in Stochastic Frontier Model," MPRA Paper 57999, University Library of Munich, Germany.
    40. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2024. "Penalized sieve estimation of zero‐inefficiency stochastic frontiers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 41-65, January.

  7. Juan Carlos Escanciano & Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2013. "Semiparametric Estimation Of Risk-Return Relationships," CAEPR Working Papers 2013-004, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    2. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  8. Van Keilegom, Ingrid & Vanhems, Anne, 2011. "Semiparametric transformation model with endogeneity: a control function approach," TSE Working Papers 11-243, Toulouse School of Economics (TSE).

    Cited by:

    1. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2017. "Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 397-420, April.
    2. Samb, R. & Heuchenne, C. & Van Keilegom, I., 2011. "Estimation of the error density in a semiparametric transformation model," LIDAM Discussion Papers ISBA 2011023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Benjamin Colling & Cédric Heuchenne & Rawane Samb & Ingrid Van Keilegom, 2015. "Estimation of the error density in a semiparametric transformation model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 1-18, February.
    4. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Discussion Papers ISBA 2013054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    6. BIRKE, Mélanie & VAN BELLEGEM, Sébastien & VAN KEILEGOM, Ingrid, 2016. "Semi-Parametric Estimation in a Single- Index Model with Endogenous Variables," LIDAM Discussion Papers CORE 2016022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Ingrid Van Keilegom, 2013. "Comments on: Model-free model-fitting and predictive distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 234-236, June.
    8. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  9. Einmahl, J.H.J. & van Keilegom, I., 2008. "Specification tests in nonparametric regression," Other publications TiSEM 2c94c2d8-8305-4fb1-b47f-7, Tilburg University, School of Economics and Management.

    Cited by:

    1. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    3. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    4. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    5. Mastromarco, Camilla & Simar, Léopold & Van Keilegom, Ingrid, 2022. "Estimating Nonparametric Conditional Frontiers and Efficiencies: A New Approach," LIDAM Discussion Papers ISBA 2022035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Van Keilegom, Ingrid, 2013. "Discussion on: "An updated review of Goodness-of-Fit tests for regression models" (by W. Gonzales-Manteiga and R.M. Crujeiras)," LIDAM Discussion Papers ISBA 2013008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Discussion Papers ISBA 2013054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Y. Andriyana & I. Gijbels & A. Verhasselt, 2018. "Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity," Statistical Papers, Springer, vol. 59(4), pages 1589-1621, December.
    9. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    10. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    11. Neumeyer, Natalie & Noh, Hohsuk & Van Keilegom, Ingrid, 2014. "Heteroscedastic semiparametric transformation models: estimation and testing for validity," LIDAM Discussion Papers ISBA 2014047, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    13. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    14. Racine, Jeffrey S. & Li, Kevin, 2017. "Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach," Journal of Econometrics, Elsevier, vol. 201(1), pages 72-94.
    15. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    16. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    17. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    18. Hlávka, Zdenek & Husková, Marie & Meintanis, Simos G., 2011. "Tests for independence in non-parametric heteroscedastic regression models," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 816-827, April.
    19. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Marek Omelka & Šárka Hudecová & Natalie Neumeyer, 2021. "Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1433-1473, December.
    21. Henderson Daniel J. & Parmeter Christopher F., 2017. "Root-n Consistent Kernel Density Estimation in Practice," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-10, January.
    22. Teran Hidalgo, Sebastian J. & Wu, Michael C. & Engel, Stephanie M. & Kosorok, Michael R., 2018. "Goodness-of-fit test for nonparametric regression models: Smoothing spline ANOVA models as example," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 135-155.

  10. Dette, Holger & Pardo-Fernandez, Juan Carlos & van Keilegom, Ingrid, 2007. "Goodness-of-fit tests for multiplicativemodels with dependent data," Technical Reports 2007,34, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Van Keilegom, Ingrid, 2013. "Discussion on: "An updated review of Goodness-of-Fit tests for regression models" (by W. Gonzales-Manteiga and R.M. Crujeiras)," LIDAM Discussion Papers ISBA 2013008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    4. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    5. Xiaohong Chen & Zhuo Huang & Yanping Yi, 2019. "Efficient Estimation of Multivariate Semi-nonparametric GARCH Filtered Copula Models," Cowles Foundation Discussion Papers 2215, Cowles Foundation for Research in Economics, Yale University.
    6. Alexander Mayer & Dominik Wied, 2021. "Estimation and Inference in Factor Copula Models with Exogenous Covariates," Papers 2107.03366, arXiv.org, revised Dec 2022.
    7. Holger Dette & Mareen Marchlewski & Jens Wagener, 2012. "Testing for a constant coefficient of variation in nonparametric regression by empirical processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 1045-1070, October.
    8. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.
    9. Han-Ying Liang & Jacobo Uña-álvarez & María Iglesias-pérez, 2015. "A Central Limit Theorem in Non-parametric Regression with Truncated, Censored and Dependent Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 256-269, March.
    10. Neumeyer, Natalie & Omelka, Marek & Hudecová, Šárka, 2019. "A copula approach for dependence modeling in multivariate nonparametric time series," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 139-162.
    11. Ingrid Van Keilegom, 2013. "Comments on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 428-431, September.

  11. Einmahl, J.H.J. & van Keilegom, I., 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.

    Cited by:

    1. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    2. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    3. Van Keilegom, Ingrid, 2013. "Discussion on: "An updated review of Goodness-of-Fit tests for regression models" (by W. Gonzales-Manteiga and R.M. Crujeiras)," LIDAM Discussion Papers ISBA 2013008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Discussion Papers ISBA 2013054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Mastromarco, Camilla & Simar, Leopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," LIDAM Reprints ISBA 2018008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Einmahl, J.H.J. & van Keilegom, I., 2004. "Goodness-of-fit Tests in Nonparametric Regression," Discussion Paper 2004-12, Tilburg University, Center for Economic Research.
    7. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    8. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    9. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    10. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    11. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    12. Hlávka, Zdenek & Husková, Marie & Meintanis, Simos G., 2011. "Tests for independence in non-parametric heteroscedastic regression models," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 816-827, April.
    13. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    14. Teran Hidalgo, Sebastian J. & Wu, Michael C. & Engel, Stephanie M. & Kosorok, Michael R., 2018. "Goodness-of-fit test for nonparametric regression models: Smoothing spline ANOVA models as example," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 135-155.

  12. Einmahl, J.H.J. & van Keilegom, I., 2006. "Goodness-of-Fit Tests in Nonparametric Regression," Discussion Paper 2006-79, Tilburg University, Center for Economic Research.

    Cited by:

    1. Sokbae Lee & Oliver Linton & Yoon-Jae Whang, 2009. "Testing for Stochastic Monotonicity," Econometrica, Econometric Society, vol. 77(2), pages 585-602, March.

  13. Dette, Holger & van Keilegom, Ingrid, 2005. "new test for the parametric form of the variance function in nonparametric regression," Technical Reports 2005,32, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Mario Francisco-Fernández & Juan Vilar-Fernández, 2009. "Two tests for heteroscedasticity in nonparametric regression," Computational Statistics, Springer, vol. 24(1), pages 145-163, February.
    2. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    3. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    4. Estate V. Khmaladze, 2021. "Distribution-free testing in linear and parametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1063-1087, December.
    5. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    6. Juan Carlos Pardo-Fernández & M. Dolores Jiménez-Gamero, 2019. "A model specification test for the variance function in nonparametric regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 387-410, September.
    7. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Joseph Ngatchou-Wandji & Marwa Ltaifa & Didier Alain Njamen Njomen & Jia Shen, 2022. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    9. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    10. Gu, Lijie & Wang, Suojin & Yang, Lijian, 2021. "Smooth simultaneous confidence band for the error distribution function in nonparametric regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    11. Einmahl, J.H.J. & van Keilegom, I., 2004. "Goodness-of-fit Tests in Nonparametric Regression," Discussion Paper 2004-12, Tilburg University, Center for Economic Research.
    12. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    13. Wang, Jiangyan & Gu, Lijie & Yang, Lijian, 2022. "Oracle-efficient estimation for functional data error distribution with simultaneous confidence band," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    14. Heuchenne, Cédric & Van Keilegom, Ingrid, 2010. "Goodness-of-fit tests for the error distribution in nonparametric regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1942-1951, August.
    15. Dette, Holger & Pardo-Fernandez, Juan Carlos & van Keilegom, Ingrid, 2007. "Goodness-of-fit tests for multiplicativemodels with dependent data," Technical Reports 2007,34, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    16. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.
    17. Natalie Neumeyer, 2009. "Smooth Residual Bootstrap for Empirical Processes of Non‐parametric Regression Residuals," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 204-228, June.
    18. Zhu, Xuehu & Guo, Xu & Lin, Lu & Zhu, Lixing, 2015. "Heteroscedasticity checks for single index models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 41-55.
    19. Kiwitt, Sebastian & Nagel, Eva-Renate & Neumeyer, Natalie, 2005. "Empirical likelihood estimators for the error distribution in nonparametric regression models," Technical Reports 2005,45, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    20. Einmahl, John H.J. & Van Keilegom, Ingrid, 2008. "Specification tests in nonparametric regression," Journal of Econometrics, Elsevier, vol. 143(1), pages 88-102, March.
    21. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Xu Guo & Tao Wang & Lixing Zhu, 2016. "Model checking for parametric single-index models: a dimension reduction model-adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1013-1035, November.
    23. Marie Hušková & Simos G. Meintanis & Charl Pretorius, 2022. "Tests for heteroskedasticity in transformation models," Statistical Papers, Springer, vol. 63(4), pages 1013-1049, August.
    24. Samarakoon, Nishantha & Song, Weixing, 2011. "Minimum distance conditional variance function checking in heteroscedastic regression models," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 579-600, March.
    25. Dette, Holger & Hetzler, Benjamin, 2008. "A martingale-transform goodness-of-fit test for the form of the conditional variance," Technical Reports 2008,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  14. Xiaohong Chen & Oliver Linton & Ingred van Keilegom, 2002. "Estimation of semiparametric models when the criterion function is not smooth," CeMMAP working papers CWP02/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Saraswata Chaudhuriy & David T. Frazierz & Eric Renault, 2016. "Indirect Inference with Endogenously Missing Exogenous Variables," CIRANO Working Papers 2016s-15, CIRANO.
    2. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    3. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    4. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    5. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    6. Xiaohong Chen & Demian Pouzo, 2009. "Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals," CeMMAP working papers CWP20/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Kyoo il Kim, 2006. "Set Inference for Semiparametric Discrete Games," Labor Economics Working Papers 22454, East Asian Bureau of Economic Research.
    8. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    10. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    11. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    12. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    13. Dong, Yingying, 2010. "Endogenous regressor binary choice models without instruments, with an application to migration," Economics Letters, Elsevier, vol. 107(1), pages 33-35, April.
    14. de Castro, Luciano & Galvao, Antonio F. & Noussair, Charles N. & Qiao, Liang, 2022. "Do people maximize quantiles?," Games and Economic Behavior, Elsevier, vol. 132(C), pages 22-40.
    15. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    16. Dennis Kristensen & Bernard Salanie, 2013. "Higher-order properties of approximate estimators," CeMMAP working papers 45/13, Institute for Fiscal Studies.
    17. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    18. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    19. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," CeMMAP working papers CWP27/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Dennis Kristensen, 2009. "Semiparametric modelling and estimation (in Russian)," Quantile, Quantile, issue 7, pages 53-83, September.
    23. Clément de Chaisemartin, 2012. "Fuzzy differences in differences," PSE Working Papers halshs-00671368, HAL.
    24. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    25. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    26. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    27. Chen, Xiaohong & Liao, Zhipeng, 2014. "Sieve M inference on irregular parameters," Journal of Econometrics, Elsevier, vol. 182(1), pages 70-86.
    28. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org.
    29. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2014. "Fuzzy Changes-in-Changes," Working Papers 2014-18, Center for Research in Economics and Statistics.
    30. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    31. Kristensen, Dennis, 2010. "Pseudo-maximum likelihood estimation in two classes of semiparametric diffusion models," Journal of Econometrics, Elsevier, vol. 156(2), pages 239-259, June.
    32. Xiaohong Chen & Yin Jia Jeff Qiu, 2016. "Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle Guide," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 259-290, October.
    33. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," Working Papers halshs-01508067, HAL.
    34. Degui Li & Zudi Lu & Oliver Linton, 2011. "Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates," Monash Econometrics and Business Statistics Working Papers 16/11, Monash University, Department of Econometrics and Business Statistics.
    35. Jeremy T. Fox & Kyoo il Kim, 2011. "A Simple Nonparametric Approach to Estimating the Distribution of Random Coefficients in Structural Models," NBER Working Papers 17283, National Bureau of Economic Research, Inc.
    36. Florens, Jean-Pierre & Johannes, Jan & Van Bellegem, Sébastien, 2009. "Instrumental Regression in Partially Linear Models," TSE Working Papers 10-167, Toulouse School of Economics (TSE).
    37. Elise Coudin & Jean-Marie Dufour, 2007. "Finite-sample Distribution-free Inference in Linear Median Regression under Heteroskedasticity and Nonlinear Dependence of Unknown Form," Working Papers 2007-38, Center for Research in Economics and Statistics.
    38. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," Papers 1411.1144, arXiv.org, revised Mar 2015.
    39. Enno Mammen & Christoph Rothe & Melanie Schienle, 2011. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers SFB649DP2011-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    40. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    41. Debopam Bhattacharya & Pascaline Dupas, 2008. "Inferring Welfare Maximizing Treatment Assignment under Budget Constraints," NBER Working Papers 14447, National Bureau of Economic Research, Inc.
    42. Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2013018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    43. Frazier, David T. & Koo, Bonsoo, 2021. "Indirect inference for locally stationary models," Journal of Econometrics, Elsevier, vol. 223(1), pages 1-27.
    44. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    45. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    47. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    48. Ferreira,Francisco H. G. & Firpo,Sergio & Galvao,Antonio F., 2017. "Estimation and inference for actual and counterfactual growth incidence curves," Policy Research Working Paper Series 7933, The World Bank.
    49. Firpo, Sergio & Galvao, Antonio F. & Song, Suyong, 2017. "Measurement errors in quantile regression models," Journal of Econometrics, Elsevier, vol. 198(1), pages 146-164.
    50. Xie, Shangyu & Wan, Alan T.K. & Zhou, Yong, 2015. "Quantile regression methods with varying-coefficient models for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 154-172.
    51. Salim Bouzebda & Issam Elhattab & Anouar Abdeldjaoued Ferfache, 2022. "General M-Estimator Processes and their m out of n Bootstrap with Functional Nuisance Parameters," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2961-3005, December.
    52. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
    53. Ji-Liang Shiu & Yingyao Hu, 2010. "Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates," Economics Working Paper Archive 557, The Johns Hopkins University,Department of Economics.
    54. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," CeMMAP working papers 38/14, Institute for Fiscal Studies.
    55. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2018. "Rationalizing Rational Expectations? Tests and Deviations," IZA Discussion Papers 11989, Institute of Labor Economics (IZA).
    56. Tadao Hoshino, 2013. "Estimation of the preference heterogeneity within stated choice data using semiparametric varying-coefficient methods," Empirical Economics, Springer, vol. 45(3), pages 1129-1148, December.
    57. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    58. Xiaohong Chen & Jinyong Hahn, 2012. "Asymptotic efficiency of semiparametric two-step GMM," CeMMAP working papers CWP31/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    59. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    60. Lena Boneva (Körber) & Oliver Linton & Michael Vogt, 2013. "The effect of fragmentation in trading on market quality in the UK equity market," CeMMAP working papers 42/13, Institute for Fiscal Studies.
    61. David T. Frazier & Bonsoo Koo, 2020. "Indirect Inference for Locally Stationary Models," Monash Econometrics and Business Statistics Working Papers 30/20, Monash University, Department of Econometrics and Business Statistics.
    62. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    63. Yingyao Hu & Arthur Lewbel, 2008. "Identifying the returns to lying when the truth is unobserved," CeMMAP working papers CWP06/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    64. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.
    65. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    66. Muller, Ursula U. & Van Keilegom, Ingrid, 2011. "Efficient parameter estimation in regression with missing responses," LIDAM Discussion Papers ISBA 2011026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    67. Vincenzo Atella & Noemi Pace & Daniela Vuri, 2008. "Are employers discriminating with respect to weight? European Evidence using Quantile Regression," CEIS Research Paper 123, Tor Vergata University, CEIS, revised 14 Jul 2008.
    68. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    69. Lena Körber & Oliver Linton & Michael Vogt, 2014. "The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market," Cambridge Working Papers in Economics 1454, Faculty of Economics, University of Cambridge.
    70. Raymond Carroll & Xiaohong Chen & Yingyao Hu, 2010. "Identification and estimation of nonlinear models using two samples with nonclassical measurement errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 419-423.
    71. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute of Labor Economics (IZA).
    72. Xiaohong Chen & Zhipeng Liao, 2015. "Sieve Semiparametric Two-Step GMM under Weak Dependence," Cowles Foundation Discussion Papers 2012, Cowles Foundation for Research in Economics, Yale University.
    73. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    74. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    75. Torgovitsky, Alexander, 2017. "Minimum distance from independence estimation of nonseparable instrumental variables models," Journal of Econometrics, Elsevier, vol. 199(1), pages 35-48.
    76. Anna Kormilitsina & Denis Nekipelov, 2015. "Consistent Variance of the Laplace Type Estimators: Application to DSGE Models," Departmental Working Papers 1510, Southern Methodist University, Department of Economics.
    77. Bravo, Francesco & Chu, Ba & Jacho-Chavez, David, 2013. "Semiparametric estimation of moment condition models with weakly dependent data," MPRA Paper 79686, University Library of Munich, Germany, revised 2016.
    78. Louise Laage, 2020. "A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity," Papers 2003.09367, arXiv.org, revised Nov 2022.
    79. Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
    80. Xiaohong Chen & Jack Favilukis & Sydney Ludvigson, 2012. "An estimation of economic models with recursive preferences," CeMMAP working papers 32/12, Institute for Fiscal Studies.
    81. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," AMSE Working Papers 1342, Aix-Marseille School of Economics, France, revised Aug 2013.
    82. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    83. Rothe, Christoph, 2016. "The Value of Knowing the Propensity Score for Estimating Average Treatment Effects," IZA Discussion Papers 9989, Institute of Labor Economics (IZA).
    84. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    85. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    86. Chen, Song Xi & Van Keilegom, Ingrid, 2013. "Estimation in semiparametric models with missing data," LIDAM Reprints ISBA 2013024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    87. Debopam Bhattacharya & Pascaline Dupas & Shin Kanaya, 2013. "Estimating the Impact of Means-tested Subsidies under Treatment Externalities with Application to Anti-Malarial Bednets," Economics Series Working Papers 646, University of Oxford, Department of Economics.
    88. Chen, Xuerong & Tang, Niansheng & Zhou, Yong, 2016. "Quantile regression of longitudinal data with informative observation times," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 176-188.
    89. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    90. Yue Zhao & Ingrid Van Keilegom & Shanshan Ding, 2022. "Envelopes for censored quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1562-1585, December.
    91. Anna Kormilitsina & Denis Nekipelov, 2016. "Consistent Variance Of The Laplace‐Type Estimators: Application To Dsge Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 603-622, May.
    92. Jad Beyhum & Jean-Pierre Florens & Ingrid Keilegom, 2023. "A nonparametric instrumental approach to confounding in competing risks models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 709-734, October.
    93. Pedro H. C. Sant'Anna & Qi Xu, 2023. "Difference-in-Differences with Compositional Changes," Papers 2304.13925, arXiv.org.
    94. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    95. Birke, M. & Van Bellegem, S. & Van Keilegom, I., 2014. "Semi-parametric estimation in a single-index model with endogenous variables," LIDAM Discussion Papers ISBA 2014043, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    238. Haitian Xie, 2022. "Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs," Papers 2204.08168, arXiv.org, revised Jul 2022.
    239. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.
    240. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.
    241. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

Articles

  1. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.

    Cited by:

    1. Jeon, Jeong Min & Van Keilegom, Ingrid, 2023. "Density estimation for mixed Euclidean and non-Euclidean data in the presence of measurement error," Journal of Multivariate Analysis, Elsevier, vol. 193(C).

  2. U U Müller & I Van Keilegom, 2019. "Goodness-of-fit tests for the cure rate in a mixture cure model," Biometrika, Biometrika Trust, vol. 106(1), pages 211-227.

    Cited by:

    1. Bo Han & Xiaoguang Wang, 2023. "Comments on: Nonparametric estimation in mixture cure models with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 496-498, June.
    2. Ana López-Cheda & Yingwei Peng & María Amalia Jácome, 2023. "Rejoinder on: Nonparametric estimation in mixture cure models with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 513-520, June.
    3. Ana López-Cheda & Yingwei Peng & María Amalia Jácome, 2023. "Nonparametric estimation in mixture cure models with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 467-495, June.
    4. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

  3. Vanhems, Anne & Van Keilegom, Ingrid, 2019. "Estimation Of A Semiparametric Transformation Model In The Presence Of Endogeneity," Econometric Theory, Cambridge University Press, vol. 35(1), pages 73-110, February.
    See citations under working paper version above.
  4. Maïlis Amico & Ingrid Van Keilegom & Catherine Legrand, 2019. "The single‐index/Cox mixture cure model," Biometrics, The International Biometric Society, vol. 75(2), pages 452-462, June.

    Cited by:

    1. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

  5. Frédérique Fève & Jean-Pierre Florens & Ingrid Van Keilegom, 2018. "Estimation of Conditional Ranks and Tests of Exogeneity in Nonparametric Nonseparable Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 334-345, April.

    Cited by:

    1. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    2. Jad Beyhum & Jean-Pierre FLorens & Ingrid Van Keilegom, 2020. "Nonparametric instrumental regression with right censored duration outcomes," Papers 2011.10423, arXiv.org.
    3. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    4. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    5. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    6. Beyhum, Jad & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2020. "Nonparametric Instrumental Regression with Right Censored Duration Outcomes," TSE Working Papers 20-1164, Toulouse School of Economics (TSE).

  6. Lan Wang & Ingrid Van Keilegom & Adam Maidman, 2018. "Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors," Biometrika, Biometrika Trust, vol. 105(4), pages 859-872.

    Cited by:

    1. Joel L. Horowitz & Ahnaf Rafi, 2023. "Bootstrap based asymptotic refinements for high-dimensional nonlinear models," Papers 2303.09680, arXiv.org, revised Feb 2024.
    2. Bondatti, Massimiliano & Rillo, Giovanni, 2022. "Commodity tail-risk and exchange rates," Finance Research Letters, Elsevier, vol. 47(PA).
    3. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    4. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
    5. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    6. Carlos Lamarche & Thomas Parker, 2022. "Wild Bootstrap Inference For Penalized Quantile Regression For Longitudinal Data," Working Papers 22003 Classification-C15,, University of Waterloo, Department of Economics.
    7. Bonaccolto, Giovanni & Borri, Nicola & Consiglio, Andrea, 2023. "Breakup and default risks in the great lockdown," Journal of Banking & Finance, Elsevier, vol. 147(C).
    8. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.

  7. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.

    Cited by:

    1. Deresa, N.W. & Van Keilegom, I. & Antonio, K., 2022. "Copula-based inference for bivariate survival data with left truncation and dependent censoring," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 1-21.
    2. Petti, Danilo & Eletti, Alessia & Marra, Giampiero & Radice, Rosalba, 2022. "Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).

  8. López-Cheda, Ana & Cao, Ricardo & Jácome, M. Amalia & Van Keilegom, Ingrid, 2017. "Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 144-165.

    Cited by:

    1. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    2. Wende Clarence Safari & Ignacio López-de-Ullibarri & María Amalia Jácome, 2023. "Latency function estimation under the mixture cure model when the cure status is available," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 608-627, July.
    3. Peizhi Li & Yingwei Peng & Ping Jiang & Qingli Dong, 2020. "A support vector machine based semiparametric mixture cure model," Computational Statistics, Springer, vol. 35(3), pages 931-945, September.
    4. Justin Chown & Cédric Heuchenne & Ingrid Van Keilegom, 2020. "The nonparametric location-scale mixture cure model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1008-1028, December.
    5. Philippe Lambert & Vincent Bremhorst, 2020. "Inclusion of time‐varying covariates in cure survival models with an application in fertility studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 333-354, January.
    6. Motahareh Parsa & Ingrid Van Keilegom, 2023. "Accelerated failure time vs Cox proportional hazards mixture cure models: David vs Goliath?," Statistical Papers, Springer, vol. 64(3), pages 835-855, June.
    7. Ana López-Cheda & Yingwei Peng & María Amalia Jácome, 2023. "Rejoinder on: Nonparametric estimation in mixture cure models with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 513-520, June.
    8. Richard Tawiah & Geoffrey J. McLachlan & Shu Kay Ng, 2020. "A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction," Biometrics, The International Biometric Society, vol. 76(3), pages 753-766, September.
    9. Ana López-Cheda & M. Amalia Jácome & Ricardo Cao, 2017. "Nonparametric latency estimation for mixture cure models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 353-376, June.
    10. Escobar-Bach, Mikael & Van Keilegom, Ingrid, 2023. "Nonparametric estimation of conditional cure models for heavy-tailed distributions and under insufficient follow-up," Computational Statistics & Data Analysis, Elsevier, vol. 183(C).
    11. Patilea, Valentin & Van Keilegom, Ingrid, 2017. "A general approach for cure models in survival analysis," LIDAM Discussion Papers ISBA 2017008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  9. Melanie Birke & Sebastien Van Bellegem & Ingrid Van Keilegom, 2017. "Semi-parametric Estimation in a Single-index Model with Endogenous Variables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 168-191, March.
    See citations under working paper version above.
  10. Juan Carlos Escanciano & Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2017. "Semiparametric Estimation of Risk–Return Relationships," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 40-52, January.
    See citations under working paper version above.
  11. Aurelie Bertrand & Catherine Legrand & Raymond J. Carroll & Christophe de Meester & Ingrid Van Keilegom, 2017. "Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach," Biometrika, Biometrika Trust, vol. 104(1), pages 31-50.

    Cited by:

    1. Chen, Li-Pang, 2019. "Semiparametric estimation for cure survival model with left-truncated and right-censored data and covariate measurement error," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    2. Amico, Mailis & Van Keilegom, Ingrid, 2017. "Cure models in survival analysis," LIDAM Discussion Papers ISBA 2017007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Bertrand, A. & Legrand, C. & Léonard, D. & Van Keilegom, I., 2017. "Robustness of estimation methods in a survival cure model with mismeasured covariates," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 3-18.

  12. Bertrand, A. & Legrand, C. & Léonard, D. & Van Keilegom, I., 2017. "Robustness of estimation methods in a survival cure model with mismeasured covariates," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 3-18.

    Cited by:

    1. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.
    2. Li, Mengyan & Ma, Yanyuan & Li, Runze, 2019. "Semiparametric regression for measurement error model with heteroscedastic error," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 320-338.
    3. Amico, Mailis & Van Keilegom, Ingrid, 2017. "Cure models in survival analysis," LIDAM Discussion Papers ISBA 2017007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Bertrand, Aurelie & Van Keilegom, Ingrid & Legrand, Catherine, 2017. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," LIDAM Discussion Papers ISBA 2017025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Han, Bo & Wang, Xiaoguang, 2020. "Semiparametric estimation for the non-mixture cure model in case-cohort and nested case-control studies," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

  13. Jean-Pierre FLORENS & Joel L. HOROWITZ & Ingrid VAN KEILEGOM, 2017. "Bias-Corrected Confidence Intervals in a Class of Linear Inverse Problems," Annals of Economics and Statistics, GENES, issue 128, pages 203-228.
    See citations under working paper version above.
  14. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.

    Cited by:

    1. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Kloodt, Nick & Neumeyer, Natalie, 2020. "Specification tests in semiparametric transformation models — A multiplier bootstrap approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    3. Mercedes Conde‐Amboage & Ingrid Van Keilegom & Wenceslao González‐Manteiga, 2021. "A new lack‐of‐fit test for quantile regression with censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 655-688, June.
    4. Marie Hušková & Simos G. Meintanis & Charl Pretorius, 2022. "Tests for heteroskedasticity in transformation models," Statistical Papers, Springer, vol. 63(4), pages 1013-1049, August.
    5. Nick Kloodt & Natalie Neumeyer & Ingrid Keilegom, 2021. "Specification testing in semi-parametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 980-1003, December.

  15. Talamakrouni, Majda & Van Keilegom, Ingrid & El Ghouch, Anouar, 2016. "Parametrically guided nonparametric density and hazard estimation with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 308-323.

    Cited by:

    1. Alghalith, Moawia, 2017. "A new parametric method of estimating the joint probability density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 799-803.
    2. Sam Efromovich & Jufen Chu, 2018. "Hazard rate estimation for left truncated and right censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 889-917, August.
    3. Moawia Alghalith, 2022. "Methods in Econophysics: Estimating the Probability Density and Volatility," Papers 2301.10178, arXiv.org.
    4. Alghalith, Moawia, 2016. "Novel and simple non-parametric methods of estimating the joint and marginal densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 94-98.

  16. Wenceslao González-Manteiga & María Dolores Martínez-Miranda & Ingrid Van Keilegom, 2016. "Goodness-of-fit test in parametric mixed effects models based on estimation of the error distribution," Biometrika, Biometrika Trust, vol. 103(1), pages 133-146.

    Cited by:

    1. Wang, Jiangyan & Gu, Lijie & Yang, Lijian, 2022. "Oracle-efficient estimation for functional data error distribution with simultaneous confidence band," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Cadirci, Mehmet Siddik & Evans, Dafydd & Leonenko, Nikolai & Makogin, Vitalii, 2022. "Entropy-based test for generalised Gaussian distributions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    3. Jakob Peterlin & Nataša Kejžar & Rok Blagus, 2023. "Correct specification of design matrices in linear mixed effects models: tests with graphical representation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 184-210, March.

  17. Simar, Léopold & Vanhems, Anne & Van Keilegom, Ingrid, 2016. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," Journal of Econometrics, Elsevier, vol. 190(2), pages 360-373.

    Cited by:

    1. Dewitte, Ruben & Dumont, Michel & Merlevede, Bruno & Rayp, Glenn & Verschelde, Marijn, 2020. "Firm-Heterogeneous Biased Technological Change: A nonparametric approach under endogeneity," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1172-1182.
    2. Frédérique Fève & Jean-Pierre Florens & Léopold Simar, 2023. "Proportional incremental cost probability functions and their frontiers," Empirical Economics, Springer, vol. 64(6), pages 2721-2756, June.
    3. Badin, Luiza & Daraio, Cinzia & Simar, Leopold, 2018. "A Bootstrap Approach for Bandwidth Selection in Estimating Conditional Efficiency Measures," LIDAM Discussion Papers ISBA 2018007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Fall, François Seck & Tchakoute Tchuigoua, Hubert & Vanhems, Anne & Simar, Léopold, 2022. "Investigating the unobserved heterogeneity effect on microfinance social efficiency," LIDAM Discussion Papers ISBA 2022010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Laurens Cherchye & Bram De Rock & Dieter Saelens & Marijn Verschelde & Bart Roets, 2022. "Performance Analysis with Unobserved Inputs: An Application to Endogenous Automation in Railway Traffic Management," Working Papers ECARES 2022-06, ULB -- Universite Libre de Bruxelles.
    6. Mastromarco, Camilla & Simar, Léopold, 2021. "Latent heterogeneity to evaluate the effect of human capital on world technology frontier," LIDAM Reprints ISBA 2021009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2015. "Testing the "Separability" Condition in Two-Stage Nonparametric Models of Production," LIDAM Discussion Papers ISBA 2015018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Jose M. Cordero & Cristina Polo & Nickolaos G. Tzeremes, 2020. "Evaluating the efficiency of municipalities in the presence of unobserved heterogeneity," Journal of Productivity Analysis, Springer, vol. 53(3), pages 377-390, June.
    9. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    10. Juan Aparicio & Jose Manuel Cordero & Carlos Díaz-Caro, 2020. "Efficiency and productivity change of regional tax offices in Spain: an empirical study using Malmquist–Luenberger and Luenberger indices," Empirical Economics, Springer, vol. 59(3), pages 1403-1434, September.
    11. Daraio, Cinzia & Simar, Léopold & Wilson, Paul W., 2021. "Quality as a latent heterogeneity factor in the efficiency of universities," Economic Modelling, Elsevier, vol. 99(C).
    12. F. S. Fall & H. Tchakoute Tchuigoua & A. Vanhems & L. Simar, 2023. "Investigating the unobserved heterogeneity effect on outreach to women: lessons from microfinance institutions," Annals of Operations Research, Springer, vol. 328(2), pages 1365-1386, September.
    13. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2019. "Quality and its impact on efficiency," LIDAM Discussion Papers ISBA 2019004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Mastromarco, Camilla & Simar, Léopold & Van Keilegom, Ingrid, 2022. "Estimating Nonparametric Conditional Frontiers and Efficiencies: A New Approach," LIDAM Discussion Papers ISBA 2022035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Cazals, Catherine & Feve, Frederique & Florens, Jean-Pierre & Simar, Leopold, 2016. "Non Parametric Instrumental Variables Estimation for Efficiency Frontier," LIDAM Reprints ISBA 2016014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. François-Seck Fall & Hubert Tchakoute Tchuigoua & Anne Vanhems & Léopold Simar, 2021. "Gender effect on microfinance social efficiency: A robust nonparametric approach," Post-Print hal-03200582, HAL.
    17. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "A novel model of costly technical efficiency," European Journal of Operational Research, Elsevier, vol. 268(2), pages 653-664.
    18. Zhongqi Deng & Qianyu Zhao & Helen X. H. Bao, 2020. "The Impact of Urbanization on Farmland Productivity: Implications for China’s Requisition–Compensation Balance of Farmland Policy," Land, MDPI, vol. 9(9), pages 1-24, September.
    19. Mastromarco, Camilla & Simar, Leopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," LIDAM Reprints ISBA 2018008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Cordero, Jose Manuel & Polo, Cristina & Simancas, Rosa, 2022. "Assessing the efficiency of secondary schools: Evidence from OECD countries participating in PISA 2015," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    21. Kristiaan Kerstens & Christopher O’donnell & Ignace van de Woestyne, 2019. "Metatechnology frontier and convexity: A restatement," Post-Print hal-02108502, HAL.
    22. Cinzia Daraio & Leopold Simar & Paul W. Wilson, 2018. "Fast and Efficient Computation of Directional Distance Estimators," LEM Papers Series 2018/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    23. D’Inverno, Giovanna & Smet, Mike & De Witte, Kristof, 2021. "Impact evaluation in a multi-input multi-output setting: Evidence on the effect of additional resources for schools," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1111-1124.
    24. Georgios Georgiadis & Ioannis Politis & Panagiotis Papaioannou, 2020. "How Does Operational Environment Influence Public Transport Effectiveness? Evidence from European Urban Bus Operators," Sustainability, MDPI, vol. 12(12), pages 1-19, June.
    25. Rouven E. Haschka & Helmut Herwartz, 2022. "Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 942-960, November.
    26. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.
    27. Walheer, Barnabé, 2023. "Meta-frontier and technology switchers: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 463-474.
    28. Devicienti, Francesco & Manello, Alessandro & Vannoni, Davide, 2016. "Technical Efficiency, Unions and Decentralized Labor Contracts," IZA Discussion Papers 10292, Institute of Labor Economics (IZA).
    29. Mastromarco, Camilla & Simar, Leopold, 2017. "Cross-Section Dependence and Latent Heterogeneity to Evaluate the Impact of Human Capital on Country Performance," LIDAM Discussion Papers ISBA 2017030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    30. Sydney Chikalipah, 2017. "Institutional Environment and Microfinance Performance in Sub-Saharan Africa," African Development Review, African Development Bank, vol. 29(1), pages 16-27, March.
    31. Benito, Bernardino & Martínez-Córdoba, Pedro-José & Guillamón, María-Dolores, 2021. "Measurement and determinants of efficiency in the municipal police service," Evaluation and Program Planning, Elsevier, vol. 85(C).
    32. Nieswand, Maria & Seifert, Stefan, 2018. "Environmental factors in frontier estimation – A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 133-148.

  18. Eduardo GarcÍa-Portugués & Ingrid Van Keilegom & Rosa M. Crujeiras and & Wenceslao González-Manteiga, 2016. "Testing parametric models in linear-directional regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1178-1191, December.

    Cited by:

    1. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    2. Di Marzio, Marco & Fensore, Stefania & Panzera, Agnese & Taylor, Charles C., 2019. "Kernel density classification for spherical data," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 23-29.
    3. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    4. Di Marzio, Marco & Fensore, Stefania & Panzera, Agnese & Taylor, Charles C., 2019. "Local binary regression with spherical predictors," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 30-36.

  19. Hohsuk Noh & Anouar El Ghouch & Ingrid Van Keilegom, 2015. "Semiparametric Conditional Quantile Estimation Through Copula-Based Multivariate Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 167-178, April.

    Cited by:

    1. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    2. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    3. Rémillard, Bruno & Nasri, Bouchra & Bouezmarni, Taoufik, 2017. "On copula-based conditional quantile estimators," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 14-20.
    4. De Backer, Mickael & El Ghouch, Anouar & Van Keilegom, Ingrid, 2016. "Semiparametric Copula Quantile Regression for Complete or Censored Data," LIDAM Discussion Papers ISBA 2016009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Kangning Wang & Wen Shan, 2021. "Copula and composite quantile regression-based estimating equations for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 441-455, June.
    6. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.

  20. Benjamin Colling & Cédric Heuchenne & Rawane Samb & Ingrid Van Keilegom, 2015. "Estimation of the error density in a semiparametric transformation model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 1-18, February.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    3. Vanhems, Anne & Van Keilegom, Ingrid, 2019. "Estimation Of A Semiparametric Transformation Model In The Presence Of Endogeneity," Econometric Theory, Cambridge University Press, vol. 35(1), pages 73-110, February.
    4. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.

  21. Kneip, Alois & Simar, Léopold & Van Keilegom, Ingrid, 2015. "Frontier estimation in the presence of measurement error with unknown variance," Journal of Econometrics, Elsevier, vol. 184(2), pages 379-393.

    Cited by:

    1. Jean-Pierre Florens & Léopold Simar & Ingrid van Keilegom, 2020. "Estimation of the Boundary of a Variable Observed with A Symmetric Error," Post-Print hal-02929524, HAL.
    2. Abdelaati Daouia & Jean-Pierre Florens & Léopold Simar, 2020. "Robust frontier estimation from noisy data: a Tikhonov regularization approach," Post-Print hal-02573853, HAL.
    3. Parmeter, Christopher F. & Simar, Léopold & Van Keilegom, Ingrid & Zelenyuk, Valentin, 2021. "Inference in the Nonparametric Stochastic Frontier Model," LIDAM Discussion Papers ISBA 2021029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances II," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 9, pages 371-408, Springer.
    5. Tsionas, Mike G., 2020. "Bounded rationality and thick frontiers in stochastic frontier analysis," European Journal of Operational Research, Elsevier, vol. 284(2), pages 762-768.
    6. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019. "Model averaging estimators for the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.
    7. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Discussion Papers ISBA 2013054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2021. "Density deconvolution with Laplace errors and unknown variance," Journal of Productivity Analysis, Springer, vol. 56(2), pages 103-113, December.
    9. Zhou, Jianhua & Parmeter, Christopher F. & Kumbhakar, Subal C., 2020. "Nonparametric estimation of the determinants of inefficiency in the presence of firm heterogeneity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1142-1152.
    10. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    11. Eric Weese & Masayoshi Hayashi & Masashi Nishikawa, 2016. "Inefficiency and Self-Determination: Simulation-based evidence from Meiji Japan," Discussion Papers 1627, Graduate School of Economics, Kobe University.
    12. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    13. Song, Junmo & Oh, Dong-hyun & Kang, Jiwon, 2017. "Robust estimation in stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 243-267.
    14. Simar, Léopold & Wilson, Paul, 2021. "Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs," LIDAM Discussion Papers ISBA 2021003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
    16. Daouia, Abdelaati & Florens, Jean-Pierre & Simar, Léopold, 2018. "Robustified expected maximum production frontiers," TSE Working Papers 17-890, Toulouse School of Economics (TSE).
    17. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    18. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    19. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2024. "Penalized sieve estimation of zero‐inefficiency stochastic frontiers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 41-65, January.

  22. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.

    Cited by:

    1. Frédérique Fève & Jean-Pierre Florens & Léopold Simar, 2023. "Proportional incremental cost probability functions and their frontiers," Empirical Economics, Springer, vol. 64(6), pages 2721-2756, June.
    2. Cinzia Daraio & Leopold Simar & Paul W. Wilson, 2016. "Nonparametric Estimation of Efficiency in the Presence of Environmental Variables," DIAG Technical Reports 2016-02, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    3. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.
    4. Mastromarco, Camilla & Simar, Léopold, 2021. "Latent heterogeneity to evaluate the effect of human capital on world technology frontier," LIDAM Reprints ISBA 2021009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2015. "Testing the "Separability" Condition in Two-Stage Nonparametric Models of Production," LIDAM Discussion Papers ISBA 2015018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Daraio, Cinzia & Simar, Léopold & Wilson, Paul W., 2021. "Quality as a latent heterogeneity factor in the efficiency of universities," Economic Modelling, Elsevier, vol. 99(C).
    7. Mastromarco, Camilla & Simar, Léopold & Zelenyuk, Valentin, 2021. "Predicting recessions with a frontier measure of output gap: an application to Italian economy," LIDAM Reprints ISBA 2021010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2019. "Quality and its impact on efficiency," LIDAM Discussion Papers ISBA 2019004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Mastromarco, Camilla & Simar, Léopold & Van Keilegom, Ingrid, 2022. "Estimating Nonparametric Conditional Frontiers and Efficiencies: A New Approach," LIDAM Discussion Papers ISBA 2022035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Camilla Mastromarco & Léopold Simar & Valentin Zelenyuk, 2019. "Predicting Recessions: A New Measure of Output Gap as Predictor," CEPA Working Papers Series WP112019, School of Economics, University of Queensland, Australia.
    11. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2013. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Discussion Papers ISBA 2013054, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. José Solana‐Ibáñez & Manuel Caravaca‐Garratón, 2021. "Stakeholder engagement and corporate social reputation: The influence of exogenous factors on efficiency performance (stakeholder engagement and exogenous factors): Stakeholder engagement and exogenou," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(6), pages 1891-1905, November.
    13. Mastromarco, Camilla & Simar, Leopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," LIDAM Reprints ISBA 2018008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Cordero, Jose Manuel & Polo, Cristina & Simancas, Rosa, 2022. "Assessing the efficiency of secondary schools: Evidence from OECD countries participating in PISA 2015," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    15. Cinzia Daraio & Leopold Simar & Paul W. Wilson, 2018. "Fast and Efficient Computation of Directional Distance Estimators," LEM Papers Series 2018/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Vidoli, Francesco & Canello, Jacopo, 2016. "Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal," European Journal of Operational Research, Elsevier, vol. 249(2), pages 771-783.
    17. Mastromarco, Camilla & Simar, Leopold, 2014. "Global Dependence and Productivity: A Robust Nonparametric World Frontier Analysis," LIDAM Discussion Papers ISBA 2014049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Mastromarco, Camilla & Simar, Leopold, 2017. "Cross-Section Dependence and Latent Heterogeneity to Evaluate the Impact of Human Capital on Country Performance," LIDAM Discussion Papers ISBA 2017030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    19. Zheng Li & Roderick M. Rejesus & Xiaoyong Zheng, 2021. "Nonparametric Estimation and Inference of Production Risk," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1857-1877, October.
    20. Simar, Leopold & Wilson, Paul, 2015. "Statistical Approaches for Nonparametric Frontier Models: A Guided Tour," LIDAM Reprints ISBA 2015022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    21. Wilson, Paul W., 2018. "Dimension reduction in nonparametric models of production," European Journal of Operational Research, Elsevier, vol. 267(1), pages 349-367.
    22. Fusco, Elisa & Vidoli, Francesco & Sahoo, Biresh K., 2018. "Spatial heterogeneity in composite indicator: A methodological proposal," Omega, Elsevier, vol. 77(C), pages 1-14.
    23. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    24. Leonardo Andrade Rocha & Ahmad Saeed Khan & Patrícia Verônica Pinheiro Sales Lima & Maria Ester Dal Poz & Fernando Porfirio Soares De Oliveira, 2016. "Corrupção, Burocracia E Outras Falhas Institucionais: O “Câncer” Da Inovação E Do Desenvolvimento," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 090, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    25. Leonardo Rocha Andrade & Leonardo Q. Cardenas & Fernando Dias Lopes & Fernando P. S. Oliveira & Kaio Cesar Fernandes, 2018. "The Impact of R&D Investments on Performance of Firms in Different Degrees of Proximity to the Technological Frontier," Economics Bulletin, AccessEcon, vol. 38(2), pages 1156-1170.

  23. Noh, Hohsuk & El Ghouch, Anouar & Van Keilegom, Ingrid, 2013. "Assessing model adequacy in possibly misspecified quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 558-569.

    Cited by:

    1. Ruosha Li & Limin Peng, 2017. "Assessing quantile prediction with censored quantile regression models," Biometrics, The International Biometric Society, vol. 73(2), pages 517-528, June.
    2. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    3. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.

  24. Song Chen & Ingrid Van Keilegom, 2013. "Estimation in semiparametric models with missing data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 785-805, August.

    Cited by:

    1. Ana M. Bianco & Graciela Boente & Wenceslao González-Manteiga & Ana Pérez-González, 2019. "Plug-in marginal estimation under a general regression model with missing responses and covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 106-146, March.
    2. Yu Shen & Han-Ying Liang, 2018. "Quantile regression and its empirical likelihood with missing response at random," Statistical Papers, Springer, vol. 59(2), pages 685-707, June.
    3. M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.

  25. Jiawei Wei & Raymond J. Carroll & Ursula U. Müller & Ingrid Van Keilegom & Nilanjan Chatterjee, 2013. "Robust estimation for homoscedastic regression in the secondary analysis of case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 185-206, January.

    Cited by:

    1. Yinghao Pan & Jianwen Cai & Sangmi Kim & Haibo Zhou, 2018. "Regression analysis for secondary response variable in a case‐cohort study," Biometrics, The International Biometric Society, vol. 74(3), pages 1014-1022, September.
    2. Naomi C. Brownstein & Jianwen Cai & Shad Smith & Luda Diatchenko & Gary D. Slade & Eric Bair, 2022. "Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies," Stats, MDPI, vol. 5(1), pages 1-12, February.
    3. Fan Zhou & Haibo Zhou & Tengfei Li & Hongtu Zhu, 2020. "Analysis of secondary phenotypes in multigroup association studies," Biometrics, The International Biometric Society, vol. 76(2), pages 606-618, June.
    4. Kartsonaki, Christiana & Cox, D. R., 2023. "Regression Reconstruction from a Retrospective Sample," Econometrics and Statistics, Elsevier, vol. 25(C), pages 87-92.

  26. Moreira, C. & Van Keilegom, I., 2013. "Bandwidth selection for kernel density estimation with doubly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 107-123.

    Cited by:

    1. Pao-sheng Shen & Yi Liu, 2019. "Pseudo maximum likelihood estimation for the Cox model with doubly truncated data," Statistical Papers, Springer, vol. 60(4), pages 1207-1224, August.
    2. Ya-Hsuan Hu & Takeshi Emura, 2015. "Maximum likelihood estimation for a special exponential family under random double-truncation," Computational Statistics, Springer, vol. 30(4), pages 1199-1229, December.
    3. Takeshi Emura & Ya-Hsuan Hu & Yoshihiko Konno, 2017. "Asymptotic inference for maximum likelihood estimators under the special exponential family with double-truncation," Statistical Papers, Springer, vol. 58(3), pages 877-909, September.
    4. Achim Dörre & Chung-Yan Huang & Yi-Kuan Tseng & Takeshi Emura, 2021. "Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model," Computational Statistics, Springer, vol. 36(1), pages 375-408, March.

  27. Luís Meira-Machado & Javier Roca-Pardiñas & Ingrid Van Keilegom & Carmen Cadarso-Suárez, 2013. "Bandwidth selection for the estimation of transition probabilities in the location-scale progressive three-state model," Computational Statistics, Springer, vol. 28(5), pages 2185-2210, October.

    Cited by:

    1. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).
    2. Jacobo de Uña-Álvarez & Luís Meira-Machado, 2015. "Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study," Biometrics, The International Biometric Society, vol. 71(2), pages 364-375, June.

  28. Hohsuk Noh & Anouar El Ghouch & Ingrid Van Keilegom, 2013. "Quality of Fit Measures in the Framework of Quantile Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(1), pages 105-118, March.

    Cited by:

    1. Ranganai, Edmore, 2016. "Quality of fit measurement in regression quantiles: An elemental set method approach," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 18-25.

  29. Liang Peng & Yongcheng Qi & Ingrid Van Keilegom, 2012. "Jackknife empirical likelihood method for copulas," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 74-92, March.

    Cited by:

    1. Ai-Ai Liu & Han-Ying Liang, 2017. "Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models," Statistical Papers, Springer, vol. 58(1), pages 95-122, March.
    2. Harold D. Chiang & Bing Yang Tan, 2020. "Empirical likelihood and uniform convergence rates for dyadic kernel density estimation," Papers 2010.08838, arXiv.org, revised May 2022.
    3. Yongli Sang & Xin Dang & Yichuan Zhao, 2020. "Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 573-598, June.
    4. Yukitoshi Matsushita & Taisuke Otsu, 2019. "Jackknife, small bandwidth and high-dimensional asymptotics," STICERD - Econometrics Paper Series 605, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Zhang, Zhigang & Zhao, Yichuan, 2013. "Empirical likelihood for linear transformation models with interval-censored failure time data," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 398-409.
    6. Yongcheng Qi, 2018. "Jackknife Empirical Likelihood Methods," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(2), pages 20-22, June.

  30. Ferraty, F. & Van Keilegom, I. & Vieu, P., 2012. "Regression when both response and predictor are functions," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 10-28.

    Cited by:

    1. Wei Wang, 2014. "Linear mixed function-on-function regression models," Biometrics, The International Biometric Society, vol. 70(4), pages 794-801, December.
    2. Graciela Boente & Daniela Rodriguez & Mariela Sued, 2018. "Testing equality between several populations covariance operators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 919-950, August.
    3. Eduardo García‐Portugués & Javier Álvarez‐Liébana & Gonzalo Álvarez‐Pérez & Wenceslao González‐Manteiga, 2021. "A goodness‐of‐fit test for the functional linear model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 502-528, June.
    4. M. D. Ruiz-Medina & D. Miranda & R. M. Espejo, 2019. "Dynamical multiple regression in function spaces, under kernel regressors, with ARH(1) errors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 943-968, September.
    5. Goldsmith, Jeff & Scheipl, Fabian, 2014. "Estimator selection and combination in scalar-on-function regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 362-372.
    6. Wang, Shaoli & Huang, Mian & Wu, Xing & Yao, Weixin, 2016. "Mixture of functional linear models and its application to CO2-GDP functional data," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 1-15.
    7. Chiou, Jeng-Min & Yang, Ya-Fang & Chen, Yu-Ting, 2016. "Multivariate functional linear regression and prediction," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 301-312.
    8. Ruiz-Medina, María D. & Álvarez-Liébana, Javier, 2019. "Strongly consistent autoregressive predictors in abstract Banach spaces," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 186-201.
    9. Joydeep Chowdhury & Probal Chaudhuri, 2020. "Convergence rates for kernel regression in infinite-dimensional spaces," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 471-509, April.
    10. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    11. Wang, Guochang & Lin, Nan & Zhang, Baoxue, 2014. "Functional k-means inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 172-182.
    12. Krebs, Johannes T.N., 2019. "The bootstrap in kernel regression for stationary ergodic data when both response and predictor are functions," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 620-639.
    13. Zhu, Hanbing & Zhang, Riquan & Yu, Zhou & Lian, Heng & Liu, Yanghui, 2019. "Estimation and testing for partially functional linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 296-314.
    14. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.
    15. Demongeot, Jacques & Naceri, Amina & Laksaci, Ali & Rachdi, Mustapha, 2017. "Local linear regression modelization when all variables are curves," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 37-44.
    16. Zhou, Zhiyang, 2021. "Fast implementation of partial least squares for function-on-function regression," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    17. Wang, Bo & Xu, Aiping, 2019. "Gaussian process methods for nonparametric functional regression with mixed predictors," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 80-90.
    18. Zhiyong Zhou & Zhengyan Lin, 2016. "Asymptotic normality of locally modelled regression estimator for functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 116-131, March.
    19. Álvarez-Liébana, J. & Bosq, D. & Ruiz-Medina, M.D., 2017. "Asymptotic properties of a component-wise ARH(1) plug-in predictor," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 12-34.
    20. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    21. Yousri Slaoui, 2021. "Recursive non-parametric kernel classification rule estimation for independent functional data," Computational Statistics, Springer, vol. 36(1), pages 79-112, March.
    22. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.
    23. Maistre, Samuel & Patilea, Valentin, 2020. "Testing for the significance of functional covariates," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    24. Heungsun Hwang & Hye Suk & Yoshio Takane & Jang-Han Lee & Jooseop Lim, 2015. "Generalized Functional Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 101-125, March.
    25. Shuzhi Zhu & Peixin Zhao, 2019. "Tests for the linear hypothesis in semi-functional partial linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 125-148, March.
    26. Ruiz-Medina, M.D. & Álvarez-Liébana, J., 2019. "A note on strong-consistency of componentwise ARH(1) predictors," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 224-228.
    27. Andrada Ivanescu & Ana-Maria Staicu & Fabian Scheipl & Sonja Greven, 2015. "Penalized function-on-function regression," Computational Statistics, Springer, vol. 30(2), pages 539-568, June.

  31. Wenceslao González‐Manteiga & Juan Carlos Pardo‐Fernández & Ingrid Van Keilegom, 2011. "ROC Curves in Non‐Parametric Location‐Scale Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 169-184, March.

    Cited by:

    1. To, Duc-Khanh & Adimari, Gianfranco & Chiogna, Monica, 2022. "Estimation of the volume under a ROC surface in presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    2. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Vanda Inácio de Carvalho & Miguel de Carvalho & Adam J. Branscum, 2017. "Nonparametric Bayesian covariate‐adjusted estimation of the Youden index," Biometrics, The International Biometric Society, vol. 73(4), pages 1279-1288, December.
    4. Pardo-Fernandez, Juan Carlos & Rodriguez-alvarez, Maria Xose & Van Keilegom, Ingrid, 2013. "A review on ROC curves in the presence of covariates," LIDAM Discussion Papers ISBA 2013050, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  32. El Ghouch, Anouar & Van Keilegom, Ingrid & McKeague, Ian W., 2011. "Empirical Likelihood Confidence Intervals For Dependent Duration Data," Econometric Theory, Cambridge University Press, vol. 27(1), pages 178-198, February.

    Cited by:

    1. Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
    2. Stelios Arvanitis & Thierry Post, 2024. "Stochastic Arbitrage Opportunities: Set Estimation and Statistical Testing," Mathematics, MDPI, vol. 12(4), pages 1-19, February.
    3. Sun, Jiajing & Hong, Yongmiao & Linton, Oliver & Zhao, Xiaolu, 2022. "Adjusted-range self-normalized confidence interval construction for censored dependent data," Economics Letters, Elsevier, vol. 220(C).
    4. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.

  33. Heuchenne, Cédric & Van Keilegom, Ingrid, 2010. "Goodness-of-fit tests for the error distribution in nonparametric regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1942-1951, August.

    Cited by:

    1. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    2. M. Jiménez Gamero, 2013. "Comments on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 412-413, September.
    3. Hambuckers, Julien & Heuchenne, Cedric, 2017. "A robust statistical approach to select adequate error distributions for financial returns," LIDAM Reprints ISBA 2017031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  34. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Györfi László & Walk Harro, 2013. "Rate of convergence of the density estimation of regression residual," Statistics & Risk Modeling, De Gruyter, vol. 30(1), pages 55-74, March.
    3. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2013. "Semiparametric Estimation of Risk-return Relationships," LIDAM Discussion Papers ISBA 2013035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Gregory S. NAMUSONGE & Mary Nelima LYANI (SINDANI) & Maurice SAKWA, 2016. "Accounts Receivable Risk Management Practices and Growth of SMEs in Kakamega County, Kenya," Expert Journal of Finance, Sprint Investify, vol. 4(1), pages 31-43.
    5. Misha Beek & Hennie Daniels, 2014. "A Non-parametric Test for Partial Monotonicity in Multiple Regression," Computational Economics, Springer;Society for Computational Economics, vol. 44(1), pages 87-100, June.
    6. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    7. Samb, R. & Heuchenne, C. & Van Keilegom, I., 2011. "Estimation of the error density in a semiparametric transformation model," LIDAM Discussion Papers ISBA 2011023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    9. Benjamin Colling & Cédric Heuchenne & Rawane Samb & Ingrid Van Keilegom, 2015. "Estimation of the error density in a semiparametric transformation model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 1-18, February.
    10. Györfi, László & Walk, Harro, 2012. "Strongly consistent density estimation of the regression residual," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1923-1929.
    11. Koul, Hira L. & Zhu, Xiaoqing, 2015. "Goodness-of-fit testing of error distribution in nonparametric ARCH(1) models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 141-160.
    12. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    13. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Joseph Ngatchou-Wandji & Marwa Ltaifa & Didier Alain Njamen Njomen & Jia Shen, 2022. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    15. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    16. Neumeyer, Natalie & Noh, Hohsuk & Van Keilegom, Ingrid, 2014. "Heteroscedastic semiparametric transformation models: estimation and testing for validity," LIDAM Discussion Papers ISBA 2014047, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Heuchenne, Cédric & Van Keilegom, Ingrid, 2010. "Goodness-of-fit tests for the error distribution in nonparametric regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1942-1951, August.
    18. Bodhisattva Sen & Mary Meyer, 2017. "Testing against a linear regression model using ideas from shape-restricted estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 423-448, March.
    19. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    20. Gabe Chandler & Wolfgang Polonik, 2017. "Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 72-98, January.
    21. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2014. "Testing for additivity in partially linear regression with possibly missing responses," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 51-61.
    22. Mary Nelima LYANI (SINDANI) & Gregory S. NAMUSONGE & Maurice SAKWA, 2016. "Accounts Receivable Risk Management Practices and Growth of SMEs in Kakamega County, Kenya," Expert Journal of Finance, Sprint Investify, vol. 4, pages 31-43.
    23. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    24. Devroye, Luc & Felber, Tina & Kohler, Michael & Krzyżak, Adam, 2012. "L1-consistent estimation of the density of residuals in random design regression models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 173-179.
    25. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    26. Irène Gijbels & Marek Omelka & Noël Veraverbeke, 2015. "Estimation of a Copula when a Covariate Affects only Marginal Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1109-1126, December.
    27. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    28. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    29. Xu Guo & Tao Wang & Lixing Zhu, 2016. "Model checking for parametric single-index models: a dimension reduction model-adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1013-1035, November.
    30. Chown, Justin, 2016. "Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 31-39.
    31. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.
    32. Pardo-Fernandez, Juan Carlos & Rodriguez-alvarez, Maria Xose & Van Keilegom, Ingrid, 2013. "A review on ROC curves in the presence of covariates," LIDAM Discussion Papers ISBA 2013050, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    33. Shuzhi Zhu & Peixin Zhao, 2019. "Tests for the linear hypothesis in semi-functional partial linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 125-148, March.
    34. Kim, Gwangsu & Choi, Taeryon, 2019. "Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 68-82.
    35. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.

  35. Frédéric Ferraty & Ingrid Van Keilegom & Philippe Vieu, 2010. "On the Validity of the Bootstrap in Non‐Parametric Functional Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 286-306, June.

    Cited by:

    1. Zhang, Xin & Wang, Chong & Wu, Yichao, 2018. "Functional envelope for model-free sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 37-50.
    2. Slaoui, Yousri, 2019. "Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 494-511.
    3. Graciela Boente & Daniela Rodriguez & Mariela Sued, 2018. "Testing equality between several populations covariance operators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 919-950, August.
    4. Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2018. "Hotelling’s T2 in separable Hilbert spaces," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 284-305.
    5. Martínez-Camblor, Pablo & Corral, Norberto, 2011. "Repeated measures analysis for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3244-3256, December.
    6. Han Shang, 2014. "Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density," Computational Statistics, Springer, vol. 29(3), pages 829-848, June.
    7. Ferraty, F. & Van Keilegom, I. & Vieu, P., 2012. "Regression when both response and predictor are functions," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 10-28.
    8. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    9. Krebs, Johannes T.N., 2019. "The bootstrap in kernel regression for stationary ergodic data when both response and predictor are functions," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 620-639.
    10. Laurent Delsol, 2013. "No effect tests in regression on functional variable and some applications to spectrometric studies," Computational Statistics, Springer, vol. 28(4), pages 1775-1811, August.
    11. Yousri Slaoui, 2020. "Recursive nonparametric regression estimation for dependent strong mixing functional data," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 665-697, October.
    12. Germán Aneiros & Paula Raña & Philippe Vieu & Juan Vilar, 2018. "Bootstrap in semi-functional partial linear regression under dependence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 659-679, September.
    13. Ling, Nengxiang & Xu, Qian, 2012. "Asymptotic normality of conditional density estimation in the single index model for functional time series data," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2235-2243.
    14. González-Rodríguez, Gil & Colubi, Ana, 2017. "On the consistency of bootstrap methods in separable Hilbert spaces," Econometrics and Statistics, Elsevier, vol. 1(C), pages 118-127.
    15. Liebl, Dominik, 2019. "Inference for sparse and dense functional data with covariate adjustments," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 315-335.
    16. Shang, Han Lin, 2016. "A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 95-104.
    17. Yousri Slaoui, 2021. "Recursive non-parametric kernel classification rule estimation for independent functional data," Computational Statistics, Springer, vol. 36(1), pages 79-112, March.
    18. Krebs, Johannes T.N., 2018. "A large deviation inequality for β-mixing time series and its applications to the functional kernel regression model," Statistics & Probability Letters, Elsevier, vol. 133(C), pages 50-58.
    19. Geenens, Gery, 2015. "Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 369-377.
    20. Manuel Febrero–Bande, 2013. "Comments on: Model-free model-fitting and predictive distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 224-226, June.
    21. Shuzhi Zhu & Peixin Zhao, 2019. "Tests for the linear hypothesis in semi-functional partial linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 125-148, March.
    22. Zhiqiang Jiang & Zhensheng Huang & Jing Zhang, 2023. "Functional single-index composite quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 595-603, July.

  36. Crujeiras, Rosa M. & Van Keilegom, Ingrid, 2010. "Least squares estimation of nonlinear spatial trends," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 452-465, February.

    Cited by:

    1. Andrea Meilán-Vila & Jean D. Opsomer & Mario Francisco-Fernández & Rosa M. Crujeiras, 2020. "A goodness-of-fit test for regression models with spatially correlated errors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 728-749, September.
    2. A. Meilán-Vila & R. Fernández-Casal & R. M. Crujeiras & M. Francisco-Fernández, 2021. "A computational validation for nonparametric assessment of spatial trends," Computational Statistics, Springer, vol. 36(4), pages 2939-2965, December.
    3. Jonathan Acosta & Felipe Osorio & Ronny Vallejos, 2016. "Effective Sample Size for Line Transect Sampling Models with an Application to Marine Macroalgae," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 407-425, September.

  37. Bianca Teodorescu & Ingrid Van Keilegom, 2010. "A goodness-of-fit test for generalised conditional linear models under left truncation and right censoring," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(5), pages 547-566.

    Cited by:

    1. Marco Munda & Catherine Legrand & Luc Duchateau & Paul Janssen, 2016. "Testing for decreasing heterogeneity in a new time-varying frailty model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 591-606, December.
    2. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    3. Legrand, Catherine & Munda, Marco & Janssen, P. & Duchateau, L., 2012. "A general class of time-varying coefficients models for right censored data," LIDAM Discussion Papers ISBA 2012041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  38. Holger Dette & Juan Carlos Pardo‐Fernández & Ingrid Van Keilegom, 2009. "Goodness‐of‐Fit Tests for Multiplicative Models with Dependent Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 782-799, December.
    See citations under working paper version above.
  39. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.

    Cited by:

    1. Gabriela Ciuperca & Zahraa Salloum, 2015. "Empirical likelihood test in a posteriori change-point nonlinear model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 919-952, November.
    2. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    3. Markevičiūtė, J., 2016. "Epidemic change tests for the mean of innovations of an AR(1) process," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 79-91.
    4. Wang, Jiangyan & Gu, Lijie & Yang, Lijian, 2022. "Oracle-efficient estimation for functional data error distribution with simultaneous confidence band," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    5. Neumeyer, Natalie & Noh, Hohsuk & Van Keilegom, Ingrid, 2014. "Heteroscedastic semiparametric transformation models: estimation and testing for validity," LIDAM Discussion Papers ISBA 2014047, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Marie Hušková & Zuzana Prášková, 2014. "Comments on: Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 265-269, June.
    7. Melanie Birke & Natalie Neumeyer, 2013. "Testing Monotonicity of Regression Functions – An Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 438-454, September.

  40. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.

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    1. Bouezmarni, Taoufik & Rombouts, Jeroen V.K. & Taamouti, Abderrahim, 2010. "Asymptotic properties of the Bernstein density copula estimator for [alpha]-mixing data," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 1-10, January.

  41. Elisa M. Molanes Lopez & Ingrid Van Keilegom & Noël Veraverbeke, 2009. "Empirical Likelihood for Non‐Smooth Criterion Functions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 413-432, September.

    Cited by:

    1. Gong, Yun & Peng, Liang & Qi, Yongcheng, 2010. "Smoothed jackknife empirical likelihood method for ROC curve," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1520-1531, July.
    2. Liang Peng & Yongcheng Qi & Ingrid Van Keilegom, 2012. "Jackknife empirical likelihood method for copulas," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 74-92, March.
    3. Liang Peng & Yongcheng Qi, 2010. "Smoothed jackknife empirical likelihood method for tail copulas," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 514-536, November.
    4. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    5. Letón, Emilio & Molanes, Elisa M., 2009. "Adjusted empirical likelihood estimation of the youden index and associated threshold for the bigamma model," DES - Working Papers. Statistics and Econometrics. WS ws091907, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Jing Sun, 2020. "An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data," Computational Statistics, Springer, vol. 35(4), pages 1621-1636, December.
    7. Yongli Sang & Xin Dang & Yichuan Zhao, 2020. "Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 573-598, June.
    8. Song Chen & Ingrid Van Keilegom, 2009. "A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 415-447, November.

  42. Song Chen & Ingrid Van Keilegom, 2009. "A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 415-447, November.

    Cited by:

    1. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2014. "High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data," MPRA Paper 59640, University Library of Munich, Germany.
    2. Tang, Cheng Yong & Leng, Chenlei, 2012. "An empirical likelihood approach to quantile regression with auxiliary information," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 29-36.
    3. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," Papers 2108.04852, arXiv.org, revised Dec 2023.
    4. Hong Guo & Changliang Zou & Zhaojun Wang & Bin Chen, 2014. "Empirical likelihood for high-dimensional linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 921-945, October.
    5. Huang, Zhensheng & Pang, Zhen & Zhang, Riquan, 2013. "Adaptive profile-empirical-likelihood inferences for generalized single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 70-82.
    6. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    7. Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
    8. Zhang, Jia & Shi, Haoming & Tian, Lemeng & Xiao, Fengjun, 2019. "Penalized generalized empirical likelihood in high-dimensional weakly dependent data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 270-283.
    9. Yves G. Berger, 2020. "An empirical likelihood approach under cluster sampling with missing observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 91-121, February.
    10. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    11. Qinqin Hu & Lu Lin, 2017. "Conditional sure independence screening by conditional marginal empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 63-96, February.
    12. Weihua Zhao & Riquan Zhang & Yukun Liu & Jicai Liu, 2015. "Empirical likelihood based modal regression," Statistical Papers, Springer, vol. 56(2), pages 411-430, May.
    13. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell, 2018. "Coverage Error Optimal Confidence Intervals for Local Polynomial Regression," Papers 1808.01398, arXiv.org, revised Jul 2021.
    14. Yongsong Qin, 2021. "Empirical Likelihood for Spatial Autoregressive Models with Spatial Autoregressive Disturbances," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 1-25, February.
    15. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    16. Zhouping Li & Yuanyuan Lin & Guoliang Zhou & Wang Zhou, 2014. "Empirical likelihood for least absolute relative error regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 86-99, March.
    17. Berger, Yves G. & Patilea, Valentin, 2022. "A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection," Econometrics and Statistics, Elsevier, vol. 24(C), pages 151-163.
    18. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    19. Yongsong Qin & Qingzhu Lei, 2021. "Empirical Likelihood for Mixed Regressive, Spatial Autoregressive Model Based on GMM," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 353-378, February.
    20. Marco Cattaneo & Andrea Wiencierz, 2014. "On the implementation of LIR: the case of simple linear regression with interval data," Computational Statistics, Springer, vol. 29(3), pages 743-767, June.
    21. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.
    23. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    24. Tong Tong Wu & Gang Li & Chengyong Tang, 2015. "Empirical Likelihood for Censored Linear Regression and Variable Selection," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 798-812, September.
    25. Jose Blanchet & Yang Kang, 2021. "Sample Out-of-Sample Inference Based on Wasserstein Distance," Operations Research, INFORMS, vol. 69(3), pages 985-1013, May.
    26. Zang, Yangguang & Zhang, Sanguo & Li, Qizhai & Zhang, Qingzhao, 2016. "Jackknife empirical likelihood test for high-dimensional regression coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 302-316.
    27. Yu Shen & Han-Ying Liang, 2018. "Quantile regression and its empirical likelihood with missing response at random," Statistical Papers, Springer, vol. 59(2), pages 685-707, June.
    28. Julia Polak & Maxwell L. King & Xibin Zhang, 2014. "A Model Validation Procedure," Monash Econometrics and Business Statistics Working Papers 21/14, Monash University, Department of Econometrics and Business Statistics.
    29. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," STICERD - Econometrics Paper Series 617, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    30. Daniela Marella & Danny Pfeffermann, 2023. "Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching," International Statistical Review, International Statistical Institute, vol. 91(2), pages 269-293, August.
    31. Ngai Chan & Liang Peng & Rongmao Zhang, 2012. "Interval estimation of the tail index of a GARCH(1,1) model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 546-565, September.
    32. Obbey Ahmed Elamin, 2018. "Impact of Informal Job-search on Wages for University Graduates in Egypt and Jordan," Working Papers 1272, Economic Research Forum, revised 19 Dec 2018.
    33. Jin, Fei & Lee, Lung-fei, 2019. "GEL estimation and tests of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 208(2), pages 585-612.
    34. Li, Minqiang & Peng, Liang & Qi, Yongcheng, 2011. "Reduce computation in profile empirical likelihood method," MPRA Paper 33744, University Library of Munich, Germany.
    35. Yves G. Berger & Ewa Kabzińska, 2020. "Empirical Likelihood Approach for Aligning Information from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 54-74, April.
    36. Hui-Ling Lin & Zhouping Li & Dongliang Wang & Yichuan Zhao, 2017. "Jackknife empirical likelihood for the error variance in linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 151-166, April.
    37. Zhang, Rongmao & Peng, Liang & Qi, Yongcheng, 2012. "Jackknife-blockwise empirical likelihood methods under dependence," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 56-72, February.

  43. Song Chen & Ingrid Van Keilegom, 2009. "Rejoinder on: A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 468-474, November.

    Cited by:

    1. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2014. "High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data," MPRA Paper 59640, University Library of Munich, Germany.
    2. Hong Guo & Changliang Zou & Zhaojun Wang & Bin Chen, 2014. "Empirical likelihood for high-dimensional linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 921-945, October.
    3. Huang, Zhensheng & Pang, Zhen & Zhang, Riquan, 2013. "Adaptive profile-empirical-likelihood inferences for generalized single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 70-82.
    4. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    5. Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
    6. Zhang, Jia & Shi, Haoming & Tian, Lemeng & Xiao, Fengjun, 2019. "Penalized generalized empirical likelihood in high-dimensional weakly dependent data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 270-283.
    7. Yves G. Berger, 2020. "An empirical likelihood approach under cluster sampling with missing observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 91-121, February.
    8. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    9. Qinqin Hu & Lu Lin, 2017. "Conditional sure independence screening by conditional marginal empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 63-96, February.
    10. Weihua Zhao & Riquan Zhang & Yukun Liu & Jicai Liu, 2015. "Empirical likelihood based modal regression," Statistical Papers, Springer, vol. 56(2), pages 411-430, May.
    11. Yongsong Qin, 2021. "Empirical Likelihood for Spatial Autoregressive Models with Spatial Autoregressive Disturbances," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 1-25, February.
    12. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    13. Zhouping Li & Yuanyuan Lin & Guoliang Zhou & Wang Zhou, 2014. "Empirical likelihood for least absolute relative error regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 86-99, March.
    14. Berger, Yves G. & Patilea, Valentin, 2022. "A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection," Econometrics and Statistics, Elsevier, vol. 24(C), pages 151-163.
    15. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. Yongsong Qin & Qingzhu Lei, 2021. "Empirical Likelihood for Mixed Regressive, Spatial Autoregressive Model Based on GMM," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 353-378, February.
    17. Marco Cattaneo & Andrea Wiencierz, 2014. "On the implementation of LIR: the case of simple linear regression with interval data," Computational Statistics, Springer, vol. 29(3), pages 743-767, June.
    18. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    19. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.
    20. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    21. Tong Tong Wu & Gang Li & Chengyong Tang, 2015. "Empirical Likelihood for Censored Linear Regression and Variable Selection," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 798-812, September.
    22. Jose Blanchet & Yang Kang, 2021. "Sample Out-of-Sample Inference Based on Wasserstein Distance," Operations Research, INFORMS, vol. 69(3), pages 985-1013, May.
    23. Zang, Yangguang & Zhang, Sanguo & Li, Qizhai & Zhang, Qingzhao, 2016. "Jackknife empirical likelihood test for high-dimensional regression coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 302-316.
    24. Yu Shen & Han-Ying Liang, 2018. "Quantile regression and its empirical likelihood with missing response at random," Statistical Papers, Springer, vol. 59(2), pages 685-707, June.
    25. Julia Polak & Maxwell L. King & Xibin Zhang, 2014. "A Model Validation Procedure," Monash Econometrics and Business Statistics Working Papers 21/14, Monash University, Department of Econometrics and Business Statistics.
    26. Daniela Marella & Danny Pfeffermann, 2023. "Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching," International Statistical Review, International Statistical Institute, vol. 91(2), pages 269-293, August.
    27. Ngai Chan & Liang Peng & Rongmao Zhang, 2012. "Interval estimation of the tail index of a GARCH(1,1) model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 546-565, September.
    28. Jin, Fei & Lee, Lung-fei, 2019. "GEL estimation and tests of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 208(2), pages 585-612.
    29. Li, Minqiang & Peng, Liang & Qi, Yongcheng, 2011. "Reduce computation in profile empirical likelihood method," MPRA Paper 33744, University Library of Munich, Germany.
    30. Yves G. Berger & Ewa Kabzińska, 2020. "Empirical Likelihood Approach for Aligning Information from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 54-74, April.
    31. Hui-Ling Lin & Zhouping Li & Dongliang Wang & Yichuan Zhao, 2017. "Jackknife empirical likelihood for the error variance in linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 151-166, April.
    32. Zhang, Rongmao & Peng, Liang & Qi, Yongcheng, 2012. "Jackknife-blockwise empirical likelihood methods under dependence," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 56-72, February.

  44. Lan Wang & Michael Akritas & Ingrid Van Keilegom, 2008. "An ANOVA-type nonparametric diagnostic test for heteroscedastic regression models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(5), pages 365-382.

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    1. Behzad Alimoradian & Jeffrey Jakubiak & Stephane Loisel & Yahia Salhi, 2023. "Understanding Key Drivers of Participant Cash Flows for Individually Managed Stable Value Funds," Risks, MDPI, vol. 11(8), pages 1-27, August.
    2. Brian Gray & Vyacheslav Lyubchich & Yulia Gel & James Rogala & Dale Robertson & Xiaoqiao Wei, 2016. "Estimation of river and stream temperature trends under haphazard sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 89-105, March.
    3. Lihong Wang, 2020. "Lack of fit test for long memory regression models," Statistical Papers, Springer, vol. 61(3), pages 1043-1067, June.
    4. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    5. Lyubchich, Vyacheslav & Gel, Yulia R., 2016. "A local factor nonparametric test for trend synchronism in multiple time series," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 91-104.

  45. Anouar El Ghouch & Ingrid Van Keilegom, 2008. "Non‐parametric Regression with Dependent Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 228-247, June.

    Cited by:

    1. Ersin Yılmaz & Syed Ejaz Ahmed & Dursun Aydın, 2020. "A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data," Stats, MDPI, vol. 3(2), pages 1-17, May.
    2. Talamakrouni, Majda & El Ghouch, Anouar & Van Keilegom, Ingrid, 2016. "Parametrically guided local quasi-likelihood with censored data," LIDAM Discussion Papers ISBA 2016011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Han-Ying Liang & Jacobo Uña-Álvarez & María Iglesias-Pérez, 2011. "Local polynomial estimation of a conditional mean function with dependent truncated data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 653-677, November.
    4. Bouhadjera Feriel & Saïd Elias Ould, 2021. "Asymptotic normality of the relative error regression function estimator for censored and time series data," Dependence Modeling, De Gruyter, vol. 9(1), pages 156-178, January.
    5. Talamakrouni, Majda & El Ghouch, Anouar & Van Keilegom, Ingrid, 2012. "Guided censored regression," LIDAM Discussion Papers ISBA 2012023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Bordes, Laurent & Gneyou, Kossi Essona, 2011. "Uniform convergence of nonparametric regressions in competing risk models with right censoring," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1654-1663, November.
    7. K. Hendrickx & P. Janssen & A. Verhasselt, 2018. "Penalized spline estimation in varying coefficient models with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 871-895, December.
    8. Han-Ying Liang & Jacobo Uña-álvarez & María Iglesias-pérez, 2015. "A Central Limit Theorem in Non-parametric Regression with Truncated, Censored and Dependent Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 256-269, March.
    9. Han-Ying Liang, 2012. "Weighted nonparametric regression estimation with truncated and dependent data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 1051-1073, December.

  46. Einmahl, John H.J. & Van Keilegom, Ingrid, 2008. "Specification tests in nonparametric regression," Journal of Econometrics, Elsevier, vol. 143(1), pages 88-102, March.
    See citations under working paper version above.
  47. Holger Dette & Natalie Neumeyer & Ingrid Van Keilegom, 2007. "A new test for the parametric form of the variance function in non‐parametric regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 903-917, November.
    See citations under working paper version above.
  48. Juan Carlos Pardo‐Fernández & Ingrid Van Keilegom, 2006. "Comparison of Regression Curves with Censored Responses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(3), pages 409-434, September.

    Cited by:

    1. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.
    2. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    3. de Uña Álvarez, Jacobo & Roca Pardiñas, Javier, 2009. "Additive models in censored regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3490-3501, July.
    4. Mercedes Conde‐Amboage & Ingrid Van Keilegom & Wenceslao González‐Manteiga, 2021. "A new lack‐of‐fit test for quantile regression with censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 655-688, June.
    5. Cédric Heuchenne & Juan Pardo-Fernández, 2012. "Testing for one-sided alternatives in nonparametric censored regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 498-518, September.

  49. César Sánchez Sellero & Wenceslao González Manteiga & Ingrid Van Keilegom, 2005. "Uniform Representation of Product‐Limit Integrals with Applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(4), pages 563-581, December.

    Cited by:

    1. Jacobo Uña-Álvarez & Noël Veraverbeke, 2013. "Generalized copula-graphic estimator," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 343-360, June.
    2. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2016. "Tree-based censored regression with applications in insurance," Post-Print hal-01141228, HAL.
    3. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 14186, Universidad del Rosario.
    4. Jacobo de Uña‐Álvarez & Micha Mandel, 2018. "Nonparametric estimation of transition probabilities for a general progressive multi‐state model under cross‐sectional sampling," Biometrics, The International Biometric Society, vol. 74(4), pages 1203-1212, December.
    5. Lopez, Olivier, 2019. "A censored copula model for micro-level claim reserving," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 1-14.
    6. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    7. Lopez, O. & Patilea, V., 2009. "Nonparametric lack-of-fit tests for parametric mean-regression models with censored data," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 210-230, January.
    8. Olivier Lopez & Valentin Patilea, 2007. "Nonparametric Lack-of-fit Tests for Parametric Mean-Regression Model with Censored Data," Working Papers 2007-01, Center for Research in Economics and Statistics.
    9. Lopez, Olivier, 2012. "A generalization of the Kaplan–Meier estimator for analyzing bivariate mortality under right-censoring and left-truncation with applications in model-checking for survival copula models," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 505-516.
    10. Gribkova, Svetlana, 2015. "Vector quantization and clustering in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 220-233.
    11. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.
    12. Cao, Ricardo & Gonzalez-Manteiga, Wenceslao, 2008. "Goodness-of-fit tests for conditional models under censoring and truncation," Journal of Econometrics, Elsevier, vol. 143(1), pages 166-190, March.
    13. Shi, Jianhua & Chen, Xiaoping & Zhou, Yong, 2015. "The strong representation for the nonparametric estimator of length-biased and right-censored data," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 49-57.
    14. Jacobo Uña-Álvarez, 2013. "Comments on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 414-418, September.
    15. Mercedes Conde‐Amboage & Ingrid Van Keilegom & Wenceslao González‐Manteiga, 2021. "A new lack‐of‐fit test for quantile regression with censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 655-688, June.
    16. Candida Geerdens & Paul Janssen & Ingrid Van Keilegom, 2020. "Goodness-of-fit test for a parametric survival function with cure fraction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 768-792, September.
    17. Olivier Lopez, 2007. "On the Estimation of the Joint Distribution in Regression Models with Censored Responses," Working Papers 2007-11, Center for Research in Economics and Statistics.

  50. Michael G. Akritas & Ingrid Van Keilegom, 2003. "Estimation of bivariate and marginal distributions with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 457-471, May.

    Cited by:

    1. Alan D. Hutson, 2016. "Nonparametric rank based estimation of bivariate densities given censored data conditional on marginal probabilities," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-14, December.
    2. Ross L. Prentice & Shanshan Zhao, 2018. "Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan–Meier estimator," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 3-27, January.
    3. Dai, Hongsheng & Bao, Yanchun, 2009. "An inverse probability weighted estimator for the bivariate distribution function under right censoring," Statistics & Probability Letters, Elsevier, vol. 79(16), pages 1789-1797, August.
    4. Svetlana Gribkova & Olivier Lopez, 2015. "Non-parametric Copula Estimation Under Bivariate Censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 925-946, December.
    5. Lopez, Olivier, 2012. "A generalization of the Kaplan–Meier estimator for analyzing bivariate mortality under right-censoring and left-truncation with applications in model-checking for survival copula models," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 505-516.
    6. Gribkova, Svetlana & Lopez, Olivier & Saint-Pierre, Philippe, 2013. "A simplified model for studying bivariate mortality under right-censoring," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 181-192.
    7. Candida Geerdens & Gerda Claeskens & Paul Janssen, 2016. "Copula based flexible modeling of associations between clustered event times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 363-381, July.
    8. Emura, Takeshi & Kao, Fan-Hsuan & Michimae, Hirofumi, 2014. "An improved nonparametric estimator of sub-distribution function for bivariate competing risk models," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 229-241.

  51. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    See citations under working paper version above.
  52. Peter Hall & Ingrid Van Keilegom, 2003. "Using difference‐based methods for inference in nonparametric regression with time series errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 443-456, May.

    Cited by:

    1. Kim, Tae Yoon & Park, Byeong U. & Moon, Myung Sang & Kim, Chiho, 2009. "Using bimodal kernel for inference in nonparametric regression with correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1487-1497, August.
    2. Wei, Honglei & Zhang, Hongfan & Jiang, Hui & Huang, Lei, 2022. "On the semi-varying coefficient dynamic panel data model with autocorrelated errors," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    3. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.
    4. Qin Shao & Lijian Yang, 2017. "Oracally efficient estimation and consistent model selection for auto-regressive moving average time series with trend," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 507-524, March.
    5. Huang, Lei & Jiang, Hui & Wang, Huixia, 2019. "A novel partial-linear single-index model for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 110-122.
    6. T. Subba Rao & Gyorgy Terdik, 2017. "A New Covariance Function and Spatio-Temporal Prediction (Kriging) for A Stationary Spatio-Temporal Random Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 936-959, November.
    7. Yuejin Zhou & Yebin Cheng & Wenlin Dai & Tiejun Tong, 2018. "Optimal difference-based estimation for partially linear models," Computational Statistics, Springer, vol. 33(2), pages 863-885, June.
    8. Krivobokova, Tatyana & Serra, Paulo & Rosales, Francisco & Klockmann, Karolina, 2022. "Joint non-parametric estimation of mean and auto-covariances for Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    9. Huan Wang & Mary C. Meyer & Jean D. Opsomer, 2013. "Constrained spline regression in the presence of AR(p) errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 809-827, December.
    10. A. Pérez-González & J. Vilar-Fernández & W. González-Manteiga, 2009. "Asymptotic properties of local polynomial regression with missing data and correlated errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 85-109, March.
    11. Inder Tecuapetla-Gómez & Axel Munk, 2017. "Autocovariance Estimation in Regression with a Discontinuous Signal and m-Dependent Errors: A Difference-Based Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 346-368, June.
    12. Lyubchich, Vyacheslav & Gel, Yulia R., 2016. "A local factor nonparametric test for trend synchronism in multiple time series," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 91-104.
    13. K De Brabanter & F Cao & I Gijbels & J Opsomer, 2018. "Local polynomial regression with correlated errors in random design and unknown correlation structure," Biometrika, Biometrika Trust, vol. 105(3), pages 681-690.
    14. Qiu, D. & Shao, Q. & Yang, L., 2013. "Efficient inference for autoregressive coefficients in the presence of trends," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 40-53.
    15. Q. Shao, 2023. "Simultaneous Confidence Band Approach for Comparison of COVID-19 Case Counts," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 372-383, July.

  53. Gang Li & Ingrid Van Keilegom, 2002. "Likelihood Ratio Confidence Bands in Non–parametric Regression with Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 547-562, September.

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    1. Junshan Shen & Shuyuan He, 2007. "Empirical likelihood for the difference of quantiles under censorship," Statistical Papers, Springer, vol. 48(3), pages 437-457, September.
    2. Shen, Junshan & Yuen, Kam Chuen & Liu, Chunling, 2016. "Empirical likelihood confidence regions for one- or two- samples with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 285-293.
    3. Arteaga-Molina, Luis A. & Rodríguez-Poo, Juan M., 2019. "Empirical likelihood based inference for a categorical varying-coefficient panel data model with fixed effects," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 110-124.
    4. Yanqing Sun & Rajeshwari Sundaram & Yichuan Zhao, 2009. "Empirical Likelihood Inference for the Cox Model with Time‐dependent Coefficients via Local Partial Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 444-462, September.
    5. Ali Al-Sharadqah & Majid Mojirsheibani, 2020. "A simple approach to construct confidence bands for a regression function with incomplete data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 81-99, March.
    6. Zhao, Yichuan & Chen, Feiming, 2008. "Empirical likelihood inference for censored median regression model via nonparametric kernel estimation," Journal of Multivariate Analysis, Elsevier, vol. 99(2), pages 215-231, February.
    7. Jimin Lee & Seunggeun Hyun, 2011. "Confidence bands for the difference of two survival functions under the additive risk model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 785-797, February.
    8. Zhao, Yichuan, 2010. "Semiparametric inference for transformation models via empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1846-1858, September.
    9. Wang, Qihua & Shen, Junshan, 2008. "Estimation and confidence bands of a conditional survival function with censoring indicators missing at random," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 928-948, May.
    10. Cheng, Guang & Zhao, Yichuan & Li, Bo, 2012. "Empirical likelihood inferences for the semiparametric additive isotonic regression," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 172-182.
    11. McKeague, Ian W. & Zhao, Yichuan, 2006. "Width-scaled confidence bands for survival functions," Statistics & Probability Letters, Elsevier, vol. 76(4), pages 327-339, February.
    12. Song Chen & Ingrid Van Keilegom, 2009. "A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 415-447, November.
    13. Yichuan Zhao & Song Yang, 2008. "Empirical likelihood inference for censored median regression with weighted empirical hazard functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 441-457, June.
    14. Junshan Shen & Shuyuan He, 2008. "Empirical likelihood confidence intervals for hazard and density functions under right censorship," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(3), pages 575-589, September.
    15. Zhao, Yichuan & Zhao, Meng, 2011. "Empirical likelihood for the contrast of two hazard functions with right censoring," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 392-401, March.

  54. Van Keilegom I. & Hettmansperger T. P., 2002. "Inference on Multivariate M Estimators Based on Bivariate Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 328-336, March.

    Cited by:

    1. Cheng, Guang & Zhou, Lan & Chen, Xiaohong & Huang, Jianhua Z., 2014. "Efficient estimation of semiparametric copula models for bivariate survival data," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 330-344.

  55. Akritas M. G & Van Keilegom I., 2001. "ANCOVA Methods for Heteroscedastic Nonparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 220-232, March.

    Cited by:

    1. Li Cai & Lijian Yang, 2015. "A smooth simultaneous confidence band for conditional variance function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 632-655, September.
    2. L. Galtchouk & S. Pergamenshchikov, 2009. "Sharp non-asymptotic oracle inequalities for non-parametric heteroscedastic regression models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(1), pages 1-18.

  56. Michael G. Akritas & Ingrid Van Keilegom, 2001. "Non‐parametric Estimation of the Residual Distribution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(3), pages 549-567, September.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Chen, Xiaohong & Linton, Oliver & Van Keilegom, Ingrid, 2003. "Estimation of semiparametric models when the criterion function is not smooth," LSE Research Online Documents on Economics 2167, London School of Economics and Political Science, LSE Library.
    3. Dette, Holger & van Keilegom, Ingrid, 2005. "new test for the parametric form of the variance function in nonparametric regression," Technical Reports 2005,32, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    5. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    6. Samb, R. & Heuchenne, C. & Van Keilegom, I., 2011. "Estimation of the error density in a semiparametric transformation model," LIDAM Discussion Papers ISBA 2011023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    8. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    9. Benjamin Colling & Cédric Heuchenne & Rawane Samb & Ingrid Van Keilegom, 2015. "Estimation of the error density in a semiparametric transformation model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 1-18, February.
    10. Györfi, László & Walk, Harro, 2012. "Strongly consistent density estimation of the regression residual," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1923-1929.
    11. Camilla Mastromarco & Léopold Simar, 2015. "Effect of FDI and Time on Catching Up: New Insights from a Conditional Nonparametric Frontier Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 826-847, August.
    12. Holger Dette & Kay Pilz, 2009. "On the estimation of a monotone conditional variance in nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 111-141, March.
    13. Portier, François & Segers, Johan, 2018. "On the weak convergence of the empirical conditional copula under a simplifying assumption," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 160-181.
    14. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    15. Einmahl, J.H.J. & van Keilegom, I., 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.
    16. Juan Mora, 2005. "The Two-Sample Problem With Regression Errors: An Empirical Process Approach," Working Papers. Serie AD 2005-18, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    17. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    18. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    19. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    20. Gu, Lijie & Wang, Suojin & Yang, Lijian, 2021. "Smooth simultaneous confidence band for the error distribution function in nonparametric regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    21. Nagel, Eva-Renate & Dette, Holger & Neumeyer, Natalie, 2004. "Bootstrap tests for the error distribution in linear and nonparametric regression models," Technical Reports 2004,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    22. Einmahl, J.H.J. & van Keilegom, I., 2004. "Goodness-of-fit Tests in Nonparametric Regression," Discussion Paper 2004-12, Tilburg University, Center for Economic Research.
    23. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    24. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    25. Wang, Jiangyan & Gu, Lijie & Yang, Lijian, 2022. "Oracle-efficient estimation for functional data error distribution with simultaneous confidence band," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    26. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    27. Neumeyer, Natalie & Noh, Hohsuk & Van Keilegom, Ingrid, 2014. "Heteroscedastic semiparametric transformation models: estimation and testing for validity," LIDAM Discussion Papers ISBA 2014047, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    28. Marie Hušková & Simos Meintanis, 2010. "Tests for the error distribution in nonparametric possibly heteroscedastic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 92-112, May.
    29. Dette, Holger & Pilz, Kay F., 2004. "On the estimation of a monotone conditional variance in nonparametric regression," Technical Reports 2004,42, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    30. Xiaohong Chen & Zhuo Huang & Yanping Yi, 2019. "Efficient Estimation of Multivariate Semi-nonparametric GARCH Filtered Copula Models," Cowles Foundation Discussion Papers 2215, Cowles Foundation for Research in Economics, Yale University.
    31. Debajit Chatterjee & Uttam Bandyopadhyay, 2019. "Testing in nonparametric ANCOVA model based on ridit reliability functional," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 327-364, April.
    32. Ivana Komunjer & Andres Santos, 2010. "Semi-parametric estimation of non-separable models: a minimum distance from independence approach," Econometrics Journal, Royal Economic Society, vol. 13(3), pages 28-55, October.
    33. Dette, Holger & Pardo-Fernandez, Juan Carlos & van Keilegom, Ingrid, 2007. "Goodness-of-fit tests for multiplicativemodels with dependent data," Technical Reports 2007,34, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    34. Alicia Pérez Alonso & Juan Mora, 2008. "Specification Tests for the Distribution of Errors in Nonoarametric Regression: A Martingale Approach," Working Papers. Serie AD 2008-11, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    35. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    36. Gabe Chandler & Wolfgang Polonik, 2017. "Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 72-98, January.
    37. Pardo-Fernández, Juan Carlos, 2007. "Comparison of error distributions in nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 77(3), pages 350-356, February.
    38. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2016. "Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors," Econometrics, MDPI, vol. 4(2), pages 1-27, April.
    39. Natalie Neumeyer, 2009. "Smooth Residual Bootstrap for Empirical Processes of Non‐parametric Regression Residuals," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 204-228, June.
    40. Emmanuel Torsen & Peter N. Mwita & Joseph K. Mungatu, 2018. "Nonparametric Estimation of the Error Functional of a Location-Scale Model," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(4), pages 1-1.
    41. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    42. Devroye, Luc & Felber, Tina & Kohler, Michael & Krzyżak, Adam, 2012. "L1-consistent estimation of the density of residuals in random design regression models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 173-179.
    43. Han Lin Shang, 2014. "Bayesian bandwidth estimation for a functional nonparametric regression model with mixed types of regressors and unknown error density," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 599-615, September.
    44. Shang, Han Lin, 2016. "A Bayesian approach for determining the optimal semi-metric and bandwidth in scalar-on-function quantile regression with unknown error density and dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 95-104.
    45. Kiwitt, Sebastian & Nagel, Eva-Renate & Neumeyer, Natalie, 2005. "Empirical likelihood estimators for the error distribution in nonparametric regression models," Technical Reports 2005,45, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    46. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    47. Sam Efromovich, 2007. "Optimal nonparametric estimation of the density of regression errors with finite support," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(4), pages 617-654, December.
    48. Caetano, Carolina & Rothe, Christoph & Yıldız, Neşe, 2016. "A discontinuity test for identification in triangular nonseparable models," Journal of Econometrics, Elsevier, vol. 193(1), pages 113-122.
    49. Neumeyer, Natalie, 2007. "A note on uniform consistency of monotone function estimators," Statistics & Probability Letters, Elsevier, vol. 77(7), pages 693-703, April.
    50. Sujica, Aleksandar & Van Keilegom, Ingrid, 2013. "Estimation of location and scale functionals in nonparametric regression under copula dependent censoring," LIDAM Discussion Papers ISBA 2013024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    51. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    52. Rothe, Christoph, 2010. "Nonparametric estimation of distributional policy effects," Journal of Econometrics, Elsevier, vol. 155(1), pages 56-70, March.
    53. Einmahl, John H.J. & Van Keilegom, Ingrid, 2008. "Specification tests in nonparametric regression," Journal of Econometrics, Elsevier, vol. 143(1), pages 88-102, March.
    54. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    55. Xiaohong Chen & Oliver Linton & Ingred van Keilegom, 2002. "Estimation of semiparametric models when the criterion function is not smooth," CeMMAP working papers 02/02, Institute for Fiscal Studies.
    56. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    57. Linton, O. & Seo, M. & Whang, Y-J., 2020. "Testing Stochastic Dominance with Many Conditioning Variables," Cambridge Working Papers in Economics 2004, Faculty of Economics, University of Cambridge.
    58. Pardo-Fernandez, Juan Carlos & Jimenez-Gamero, Maria Dolores & El Ghouch, Anouar, 2013. "A nonparametric ANOVA-type test for regression curves based on characteristic functions," LIDAM Discussion Papers ISBA 2013022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    59. Ingrid Keilegom & Wenceslao González Manteiga & César Sánchez Sellero, 2008. "Goodness-of-fit tests in parametric regression based on the estimation of the error distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 401-415, August.
    60. Dimitris Politis, 2013. "Rejoinder on: Model-free model-fitting and predictive distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 240-250, June.
    61. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.
    62. Müller Ursula U. & Schick Anton & Wefelmeyer Wolfgang, 2007. "Estimating the error distribution function in semiparametric regression," Statistics & Risk Modeling, De Gruyter, vol. 25(1/2007), pages 1-18, January.
    63. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    64. Gonzales Manteiga, Wenceslao & Maria Dolores, Martinez Miranda & Van Keilegom, Ingrid, 2012. "Goodness-of-fit Test in Parametric Mixed-Effects Models based on the Estimation of the Error Distribution," LIDAM Discussion Papers ISBA 2012022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    65. Patilea, Valentin & Van Keilegom, Ingrid, 2017. "A general approach for cure models in survival analysis," LIDAM Discussion Papers ISBA 2017008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    66. Neumeyer, Natalie, 2005. "A note on uniform consistency of monotone function estimators," Technical Reports 2005,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    67. Melanie Birke & Natalie Neumeyer, 2013. "Testing Monotonicity of Regression Functions – An Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 438-454, September.

  57. Ingrid Van Keilegom & Noël Veraverbeke, 2001. "Hazard Rate Estimation in Nonparametric Regression with Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages 730-745, December.

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    1. Steven Abrams & Paul Janssen & Jan Swanepoel & Noël Veraverbeke, 2020. "Nonparametric estimation of the cross ratio function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 771-801, June.
    2. Ricardo Cao & Paul Janssen & Noël Veraverbeke, 2005. "Relative hazard rate estimation for right censored and left truncated data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 257-280, June.
    3. Gneyou, Kossi Essona, 2014. "A strong linear representation for the maximum conditional hazard rate estimator in survival analysis," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 10-18.
    4. Spierdijk, Laura, 2008. "Nonparametric conditional hazard rate estimation: A local linear approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2419-2434, January.
    5. Han-Ying Liang & Elias Ould Saïd, 2018. "A weighted estimator of conditional hazard rate with left-truncated and dependent data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 155-189, February.
    6. Fuxia Cheng, 2017. "Asymptotic Properties of Hazard Rate Estimator in Censored Linear Regression," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 1-12, February.
    7. Tine Buch-Kromann & Jens Nielsen, 2012. "Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 167-192, February.
    8. Kadiri Nadia & Rabhi Abbes & Bouchentouf Amina Angelika, 2018. "Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship," Dependence Modeling, De Gruyter, vol. 6(1), pages 197-227, November.

  58. Ingrid Van Keilegom & Noël Veraverbeke, 1997. "Estimation and Bootstrap with Censored Data in Fixed Design Nonparametric Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(3), pages 467-491, September.

    Cited by:

    1. Braekers, Roel & Veraverbeke, Noël, 2008. "A conditional Koziol-Green model under dependent censoring," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 927-937, May.
    2. Jacobo Uña-Álvarez & Noël Veraverbeke, 2013. "Generalized copula-graphic estimator," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 343-360, June.
    3. Sujica, Aleksandar & Van Keilegom, Ingrid, 2018. "The copula-graphic estimator in censored nonparametric location-scale regression models," Econometrics and Statistics, Elsevier, vol. 7(C), pages 89-114.
    4. Cédric Heuchenne & Ingrid Keilegom, 2010. "Estimation in nonparametric location-scale regression models with censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(3), pages 439-463, June.
    5. Ingrid Van Keilegom & Noël Veraverbeke, 2001. "Hazard Rate Estimation in Nonparametric Regression with Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages 730-745, December.
    6. Rebeca Peláez & Ricardo Cao & Juan M. Vilar, 2022. "Bootstrap Bandwidth Selection and Confidence Regions for Double Smoothed Default Probability Estimation," Mathematics, MDPI, vol. 10(9), pages 1-25, May.
    7. Lopez-Cheda , Ana & Cao, Ricardo & Jacome, Maria Amalia & Van Keilegom, Ingrid, 2015. "Nonparametric incidence and latency estimation in mixture cure models," LIDAM Discussion Papers ISBA 2015014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Majda Talamakrouni & Anouar El Ghouch & Ingrid Van Keilegom, 2015. "Guided Censored Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 214-233, March.
    9. Abegaz, Fentaw & Gijbels, Irène & Veraverbeke, Noël, 2012. "Semiparametric estimation of conditional copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 43-73.
    10. Cao, Ricardo & Gonzalez-Manteiga, Wenceslao, 2008. "Goodness-of-fit tests for conditional models under censoring and truncation," Journal of Econometrics, Elsevier, vol. 143(1), pages 166-190, March.
    11. Sujica, Aleksandar & Van Keilegom, Ingrid, 2013. "Estimation of location and scale functionals in nonparametric regression under copula dependent censoring," LIDAM Discussion Papers ISBA 2013024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Noel Veraverbeke & Carmen Cadarso-Suárez, 2000. "Estimation of the conditional distribution in a conditional Koziol-green model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 97-122, June.
    13. López-Cheda, Ana & Cao, Ricardo & Jácome, M. Amalia & Van Keilegom, Ingrid, 2017. "Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 144-165.
    14. Heuchenne, Cédric & Van Keilegom, Ingrid, 2007. "Location estimation in nonparametric regression with censored data," Journal of Multivariate Analysis, Elsevier, vol. 98(8), pages 1558-1582, September.
    15. Mercedes Conde‐Amboage & Ingrid Van Keilegom & Wenceslao González‐Manteiga, 2021. "A new lack‐of‐fit test for quantile regression with censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 655-688, June.
    16. Escobar-Bach, Mikael & Van Keilegom, Ingrid, 2023. "Nonparametric estimation of conditional cure models for heavy-tailed distributions and under insufficient follow-up," Computational Statistics & Data Analysis, Elsevier, vol. 183(C).
    17. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    18. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1275-1306, December.
    19. Olivier Lopez, 2007. "On the Estimation of the Joint Distribution in Regression Models with Censored Responses," Working Papers 2007-11, Center for Research in Economics and Statistics.
    20. Keilegom, Ingrid Van & Akritas, Michael G. & Veraverbeke, Noel, 2001. "Estimation of the conditional distribution in regression with censored data: a comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 487-500, February.
    21. M. Iglesias Pérez & W. González Manteiga, 2003. "Bootstrap for the conditional distribution function with truncated and censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 331-357, June.

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