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Peter Filzmoser

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. Fritz, H. & Filzmoser, P. & Croux, C., 2010. "A Comparison of Algorithms for the Multivariate L1-Median," Discussion Paper 2010-106, Tilburg University, Center for Economic Research.

    Cited by:

    1. Filzmoser, P. & Höppner, S. & Ortner, I. & Serneels, S. & Verdonck, T., 2020. "Cellwise robust M regression," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    2. Oluwasegun Taiwo Ojo & Antonio Fernández Anta & Rosa E. Lillo & Carlo Sguera, 2022. "Detecting and classifying outliers in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 725-760, September.
    3. C. Chatzinakos & L. Pitsoulis & G. Zioutas, 2016. "Optimization techniques for robust multivariate location and scatter estimation," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1443-1460, May.

  2. Peter Filzmoser & Catherine Dehon & Christophe Croux, 2000. "Outlier resistant estimators for canonical correlation analysis," ULB Institutional Repository 2013/8460, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    2. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    3. Adrover, Jorge G. & Donato, Stella M., 2015. "A robust predictive approach for canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 356-376.

  3. Catherine Dehon & Peter Filzmoser & Christophe Croux, 2000. "Robust methods for canonical correlation analysis," ULB Institutional Repository 2013/8458, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Coleman Jacob & Replogle Joseph & Chandler Gabriel & Hardin Johanna, 2016. "Resistant multiple sparse canonical correlation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(2), pages 123-138, April.

Articles

  1. Šárka Brodinová & Peter Filzmoser & Thomas Ortner & Christian Breiteneder & Maia Rohm, 2019. "Robust and sparse k-means clustering for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 905-932, December.

    Cited by:

    1. Mauricio Toledo-Acosta & Talin Barreiro & Asela Reig-Alamillo & Markus Müller & Fuensanta Aroca Bisquert & Maria Luisa Barrigon & Enrique Baca-Garcia & Jorge Hermosillo-Valadez, 2020. "Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    2. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.

  2. D. Rosadi & P. Filzmoser, 2019. "Robust second-order least-squares estimation for regression models with autoregressive errors," Statistical Papers, Springer, vol. 60(1), pages 105-122, February.

    Cited by:

    1. Lei He & Rong-Xian Yue, 2022. "$$I_L$$ I L -optimal designs for regression models under the second-order least squares estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 53-66, January.
    2. Mustafa Salamh & Liqun Wang, 2021. "Second-Order Least Squares Estimation in Nonlinear Time Series Models with ARCH Errors," Econometrics, MDPI, vol. 9(4), pages 1-17, November.

  3. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 785-822, September.

    Cited by:

    1. M. Rosário Oliveira & Margarida Azeitona & António Pacheco & Rui Valadas, 2022. "Association measures for interval variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 491-520, September.

  4. Sara de la Rosa de Sáa & María Asunción Lubiano & Beatriz Sinova & Peter Filzmoser, 2017. "Robust scale estimators for fuzzy data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 731-758, December.

    Cited by:

    1. Beatriz Sinova & Stefan Van Aelst & Pedro Terán, 2021. "M-estimators and trimmed means: from Hilbert-valued to fuzzy set-valued data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 267-288, June.

  5. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.

    Cited by:

    1. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    2. Dorothea Dumuid & Željko Pedišić & Javier Palarea-Albaladejo & Josep Antoni Martín-Fernández & Karel Hron & Timothy Olds, 2020. "Compositional Data Analysis in Time-Use Epidemiology: What, Why, How," IJERPH, MDPI, vol. 17(7), pages 1-17, March.

  6. Andreas Alfons & Christophe Croux & Peter Filzmoser, 2017. "Robust Maximum Association Estimators," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 436-445, January.

    Cited by:

    1. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    2. Nathan Uyttendaele, 2018. "On the estimation of nested Archimedean copulas: a theoretical and an experimental comparison," Computational Statistics, Springer, vol. 33(2), pages 1047-1070, June.
    3. Langworthy, Benjamin W. & Stephens, Rebecca L. & Gilmore, John H. & Fine, Jason P., 2021. "Canonical correlation analysis for elliptical copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    4. Jorge G. Adrover & Stella M. Donato, 2023. "Aspects of robust canonical correlation analysis, principal components and association," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 623-650, June.
    5. Alfio Marazzi & Marina Valdora & Victor Yohai & Michael Amiguet, 2019. "A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 223-241, March.
    6. Liebscher, Eckhard, 2021. "Kendall regression coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    7. Stephen P. Groff, 2022. "A contemporary social contract: An exploration of enabling factors influencing climate policy intractability in developed nations," Global Policy, London School of Economics and Political Science, vol. 13(5), pages 721-735, November.

  7. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.

    Cited by:

    1. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
    2. Tadao Hoshino, 2024. "Functional Spatial Autoregressive Models," Papers 2402.14763, arXiv.org.
    3. 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.
    4. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01821815, HAL.
    5. Karel Hron & Jitka Machalová & Alessandra Menafoglio, 2023. "Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation," Statistical Papers, Springer, vol. 64(5), pages 1629-1667, October.
    6. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
    7. Jitka Machalová & Renáta Talská & Karel Hron & Aleš Gába, 2021. "Compositional splines for representation of density functions," Computational Statistics, Springer, vol. 36(2), pages 1031-1064, June.
    8. Marco Stefanucci & Stefano Mazzuco, 2022. "Analysing cause‐specific mortality trends using compositional functional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 61-83, January.
    9. Dominique Guégan & Matteo Iacopini, 2018. "Nonparameteric forecasting of multivariate probability density functions," Documents de travail du Centre d'Economie de la Sorbonne 18012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    10. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.
    11. Matteo Iacopini & Dominique Guégan, 2018. "Nonparametric Forecasting of Multivariate Probability Density Functions," Working Papers 2018:15, Department of Economics, University of Venice "Ca' Foscari".
    12. ARATA Yoshiyuki, 2017. "A Functional Linear Regression Model in the Space of Probability Density Functions," Discussion papers 17015, Research Institute of Economy, Trade and Industry (RIETI).
    13. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
    14. Łukasz Smaga & Jin‐Ting Zhang, 2020. "Linear hypothesis testing for weighted functional data with applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 493-515, June.
    15. Petersen, Alexander & Zhang, Chao & Kokoszka, Piotr, 2022. "Modeling Probability Density Functions as Data Objects," Econometrics and Statistics, Elsevier, vol. 21(C), pages 159-178.
    16. Genest, Christian & Hron, Karel & Nešlehová, Johanna G., 2023. "Orthogonal decomposition of multivariate densities in Bayes spaces and relation with their copula-based representation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    17. Chao Zhang & Piotr Kokoszka & Alexander Petersen, 2022. "Wasserstein autoregressive models for density time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 30-52, January.
    18. Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Post-Print halshs-01821815, HAL.
    19. Seo, Won-Ki & Beare, Brendan K., 2019. "Cointegrated linear processes in Bayes Hilbert space," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 90-95.
    20. Talská, R. & Menafoglio, A. & Machalová, J. & Hron, K. & Fišerová, E., 2018. "Compositional regression with functional response," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 66-85.

  8. Petra Kynčlová & Peter Filzmoser & Karel Hron, 2015. "Modeling Compositional Time Series with Vector Autoregressive Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 303-314, July.

    Cited by:

    1. Juan David Vega Baquero & Miguel Santolino, 2021. ""Too big to fail? An analysis of the Colombian banking system through compositional data"," IREA Working Papers 202111, University of Barcelona, Research Institute of Applied Economics, revised Apr 2021.
    2. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    3. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    4. Wiktor R. Żelazny & Tomáš Šimon, 2022. "Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations," Agriculture, MDPI, vol. 12(5), pages 1-26, May.
    5. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    6. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    7. Jiajia Chen & Xiaoqin Zhang & Shengjia Li, 2017. "Multiple linear regression with compositional response and covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2270-2285, September.
    8. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    9. Morais, Joanna & Simioni, Michel & Thomas-Agnan, Christine, 2016. "A tour of regression models for explaining shares," TSE Working Papers 16-742, Toulouse School of Economics (TSE).
    10. Juan M.C. Larrosa, 2017. "Compositional Time Series: Past and Perspectives," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 1, pages 1-1, June.
    11. Vega Baquero, Juan David & Santolino, Miguel, 2022. "Too big to fail? An analysis of the Colombian banking system through compositional data," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).

  9. M. Templ & P. Filzmoser, 2014. "Simulation and quality of a synthetic close-to-reality employer--employee population," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1053-1072, May.

    Cited by:

    1. Roszka Wojciech, 2019. "Spatial Microsimulation Of Personal Income In Poland At The Level Of Subregions," Statistics in Transition New Series, Polish Statistical Association, vol. 20(3), pages 133-153, September.
    2. Amanda M. Y. Chu & Benson S. Y. Lam & Agnes Tiwari & Mike K. P. So, 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research," IJERPH, MDPI, vol. 16(22), pages 1-17, November.

  10. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(1), pages 187-207, February.

    Cited by:

    1. Ning Li & Hu Yang, 2021. "Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models," Statistical Papers, Springer, vol. 62(2), pages 661-680, April.
    2. G. S. Monti & P. Filzmoser, 2022. "Robust logistic zero-sum regression for microbiome compositional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 301-324, June.
    3. Adriano Zanin Zambom & Gregory J. Matthews, 2021. "Sure independence screening in the presence of missing data," Statistical Papers, Springer, vol. 62(2), pages 817-845, April.
    4. Heewon Park & Sadanori Konishi, 2017. "Principal component selection via adaptive regularization method and generalized information criterion," Statistical Papers, Springer, vol. 58(1), pages 147-160, March.
    5. Jan Pablo Burgard & Joscha Krause & Dennis Kreber & Domingo Morales, 2021. "The generalized equivalence of regularization and min–max robustification in linear mixed models," Statistical Papers, Springer, vol. 62(6), pages 2857-2883, December.
    6. Jun Lu & Lu Lin, 2020. "Model-free conditional screening via conditional distance correlation," Statistical Papers, Springer, vol. 61(1), pages 225-244, February.
    7. Li Liu & Hao Wang & Yanyan Liu & Jian Huang, 2021. "Model pursuit and variable selection in the additive accelerated failure time model," Statistical Papers, Springer, vol. 62(6), pages 2627-2659, December.
    8. Jianbo Li & Yuan Li & Riquan Zhang, 2017. "B spline variable selection for the single index models," Statistical Papers, Springer, vol. 58(3), pages 691-706, September.
    9. Sun, Hongwei & Cui, Yuehua & Gao, Qian & Wang, Tong, 2020. "Trimmed LASSO regression estimator for binary response data," Statistics & Probability Letters, Elsevier, vol. 159(C).

  11. Peter Filzmoser & Anne Ruiz-Gazen & Christine Thomas-Agnan, 2014. "Identification of local multivariate outliers," Statistical Papers, Springer, vol. 55(1), pages 29-47, February.

    Cited by:

    1. Mohammed Attouch & Ali Laksaci & Nafissa Messabihi, 2017. "Nonparametric relative error regression for spatial random variables," Statistical Papers, Springer, vol. 58(4), pages 987-1008, December.
    2. Brenton R. Clarke & Andrew Grose, 2023. "A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering," Statistical Papers, Springer, vol. 64(2), pages 395-420, April.
    3. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "Rejoinder to the discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 661-666, December.
    4. Fernanda De Bastiani & Audrey Mariz de Aquino Cysneiros & Miguel Uribe-Opazo & Manuel Galea, 2015. "Influence diagnostics in elliptical spatial linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 322-340, June.
    5. Tarr, G. & Müller, S. & Weber, N.C., 2016. "Robust estimation of precision matrices under cellwise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 404-420.

  12. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Erratum to: Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(3), pages 917-918, August.

    Cited by:

    1. Ning Li & Hu Yang, 2021. "Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models," Statistical Papers, Springer, vol. 62(2), pages 661-680, April.
    2. G. S. Monti & P. Filzmoser, 2022. "Robust logistic zero-sum regression for microbiome compositional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 301-324, June.
    3. Adriano Zanin Zambom & Gregory J. Matthews, 2021. "Sure independence screening in the presence of missing data," Statistical Papers, Springer, vol. 62(2), pages 817-845, April.
    4. Heewon Park & Sadanori Konishi, 2017. "Principal component selection via adaptive regularization method and generalized information criterion," Statistical Papers, Springer, vol. 58(1), pages 147-160, March.
    5. Jan Pablo Burgard & Joscha Krause & Dennis Kreber & Domingo Morales, 2021. "The generalized equivalence of regularization and min–max robustification in linear mixed models," Statistical Papers, Springer, vol. 62(6), pages 2857-2883, December.
    6. Jun Lu & Lu Lin, 2020. "Model-free conditional screening via conditional distance correlation," Statistical Papers, Springer, vol. 61(1), pages 225-244, February.
    7. Li Liu & Hao Wang & Yanyan Liu & Jian Huang, 2021. "Model pursuit and variable selection in the additive accelerated failure time model," Statistical Papers, Springer, vol. 62(6), pages 2627-2659, December.
    8. Jianbo Li & Yuan Li & Riquan Zhang, 2017. "B spline variable selection for the single index models," Statistical Papers, Springer, vol. 58(3), pages 691-706, September.
    9. Sun, Hongwei & Cui, Yuehua & Gao, Qian & Wang, Tong, 2020. "Trimmed LASSO regression estimator for binary response data," Statistics & Probability Letters, Elsevier, vol. 159(C).

  13. Andreas Alfons & Matthias Templ & Peter Filzmoser, 2013. "Robust estimation of economic indicators from survey samples based on Pareto tail modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 271-286, March.

    Cited by:

    1. Winkelried, Diego & Escobar, Bruno, 2020. "Declining inequality in Latin America? Robustness checks for Peru," MPRA Paper 106566, University Library of Munich, Germany.
    2. Kris Boudt & Valentin Todorov & Wenjing Wang, 2020. "Robust Distribution-Based Winsorization in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 375-397, June.
    3. Xavier Jara & Nicolás Oliva, 2018. "Top income adjustments and tax reforms in Ecuador," WIDER Working Paper Series wp-2018-165, World Institute for Development Economic Research (UNU-WIDER).
    4. Brzeziński, Michał & Myck, Michal & Najsztub, Mateusz, 2019. "Reevaluating Distributional Consequences of the Transition to Market Economy in Poland: New Results from Combined Household Survey and Tax Return Data," IZA Discussion Papers 12734, Institute of Labor Economics (IZA).
    5. Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2018. "Survey Under‐Coverage of Top Incomes and Estimation of Inequality: What is the Role of the UK's SPI Adjustment?," Fiscal Studies, John Wiley & Sons, vol. 39(2), pages 213-240, June.
    6. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    7. Richard Bluhm & Melanie Krause, 2020. "Top Lights: Bright cities and their contribution to economic development," SoDa Laboratories Working Paper Series 2020-08, Monash University, SoDa Laboratories.
    8. Nora Lustig, 2018. "Measuring the Distribution of Household Income, Consumption and Wealth: State of Play and Measurement Challenges," Working Papers 1801, Tulane University, Department of Economics.
    9. SOLOGON Denisa & VAN KERM Philippe, 2014. "Earnings dynamics, foreign workers and the stability of inequality trends in Luxembourg 1988-2009," LISER Working Paper Series 2014-03, Luxembourg Institute of Socio-Economic Research (LISER).
    10. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "A robust semi-parametric approach for measuring income inequality in Malaysia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1-13.
    11. Sofie R. Waltl & Robin Chakraborty, 2022. "Missing the wealthy in the HFCS: micro problems with macro implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 169-203, March.
    12. Maria Marino & Benedetto Rocchi & Simone Severini, 2021. "Conditional Income Disparity between Farm and Non‐farm Households in the European Union: A Longitudinal Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 589-606, June.
    13. Stephen P. Jenkins, 2017. "Pareto Models, Top Incomes and Recent Trends in UK Income Inequality," Economica, London School of Economics and Political Science, vol. 84(334), pages 261-289, April.
    14. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
    15. Vladimir Hlasny, 2020. "Parametric Representation of the Top of Income Distributions: Options, Historical Evidence and Model Selection," Commitment to Equity (CEQ) Working Paper Series 90, Tulane University, Department of Economics.
    16. Jan Pablo Burgard & Joscha Krause & Dennis Kreber & Domingo Morales, 2021. "The generalized equivalence of regularization and min–max robustification in linear mixed models," Statistical Papers, Springer, vol. 62(6), pages 2857-2883, December.
    17. Benedetto Rocchi & Maria Marino & Simone Severini, 2021. "Does an Income Gap between Farm and Nonfarm Households Still Exist? The Case of the European Union," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1672-1697, December.
    18. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "Optimal threshold for Pareto tail modelling in the presence of outliers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 169-180.
    19. Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.
    20. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.
    21. P. Jenkins, Stephen & Hérault, Nicolas & V. Burkhauser, Richard & Wilkins, Roger, 2017. "Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment?," ISER Working Paper Series 2017-08, Institute for Social and Economic Research.
    22. Li, Chengyou & Yu, Yangcheng & Li, Qinghai, 2021. "Top-income data and income inequality correction in China," Economic Modelling, Elsevier, vol. 97(C), pages 210-219.
    23. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    24. Frank A. Cowell & Philippe Kerm, 2015. "Wealth Inequality: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(4), pages 671-710, September.
    25. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    26. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
    27. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    28. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2019. "A robust and efficient estimator for the tail index of inverse Pareto distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 431-439.
    29. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
    30. Michał Brzeziński, 2013. "Robust estimation of the Pareto index: A Monte Carlo Analysis," Working Papers 2013-32, Faculty of Economic Sciences, University of Warsaw.

  14. Neykov, N.M. & Filzmoser, P. & Neytchev, P.N., 2012. "Robust joint modeling of mean and dispersion through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 34-48, January.

    Cited by:

    1. Alfio Marazzi, 2021. "Improving the Efficiency of Robust Estimators for the Generalized Linear Model," Stats, MDPI, vol. 4(1), pages 1-20, February.
    2. Yee, Thomas W., 2014. "Reduced-rank vector generalized linear models with two linear predictors," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 889-902.
    3. Neykov, N.M. & Čížek, P. & Filzmoser, P. & Neytchev, P.N., 2012. "The least trimmed quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1757-1770.
    4. Atkinson, Anthony C. & Riani, Marco & Torti, Francesca, 2016. "Robust methods for heteroskedastic regression," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 209-222.
    5. Pitselis, Georgios, 2017. "Risk measures in a quantile regression credibility framework with Fama/French data applications," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 122-134.

  15. Martín-Fernández, J.A. & Hron, K. & Templ, M. & Filzmoser, P. & Palarea-Albaladejo, J., 2012. "Model-based replacement of rounded zeros in compositional data: Classical and robust approaches," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2688-2704.

    Cited by:

    1. Theophilus K. Agbenyezi & Gordon Foli & Simon K. Y. Gawu, 2020. "Geochemical Characteristics Of Gold-Bearing Granitoids At Ayanfuri In The Kumasi Basin, Southwestern Ghana: Implications For The Orogenic Related Gold Systems," Earth Sciences Malaysia (ESMY), Zibeline International Publishing, vol. 4(2), pages 127-134, June.
    2. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
    3. Tsagris, Michail, 2014. "The k-NN algorithm for compositional data: a revised approach with and without zero values present," MPRA Paper 65866, University Library of Munich, Germany.
    4. Tsagris, Michail, 2015. "Regression analysis with compositional data containing zero values," MPRA Paper 67868, University Library of Munich, Germany.
    5. Germ`a Coenders & N'uria Arimany Serrat, 2023. "Accounting statement analysis at industry level. A gentle introduction to the compositional approach," Papers 2305.16842, arXiv.org, revised Feb 2024.
    6. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.

  16. Heinrich Fritz & Peter Filzmoser & Christophe Croux, 2012. "A comparison of algorithms for the multivariate L 1 -median," Computational Statistics, Springer, vol. 27(3), pages 393-410, September.
    See citations under working paper version above.
  17. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.

    Cited by:

    1. Dargel, Lukas & Thomas-Agnan, Christine, 2023. "Share-ratio interpretations of compositional regression models," TSE Working Papers 23-1456, Toulouse School of Economics (TSE), revised 20 Sep 2023.
    2. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unraveling the predictive power of telematics data in car insurance pricing," Working Papers of Department of Decision Sciences and Information Management, Leuven 618916, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    3. Zhao, T.; & Sutton, M.; & Meacock, M.;, 2023. "Use of compositional covariates in linear regression: problems and solutions," Health, Econometrics and Data Group (HEDG) Working Papers 23/16, HEDG, c/o Department of Economics, University of York.
    4. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    5. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 869-909, December.
    6. Haixiang Zhang & Jun Chen & Zhigang Li & Lei Liu, 2021. "Testing for Mediation Effect with Application to Human Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 313-328, July.
    7. Biyun Guo & Taiping Xie & M.V. Subrahmanyam, 2019. "The Impact of China’s Grain for Green Program on Rural Economy and Precipitation: A Case Study of Yan River Basin in the Loess Plateau," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    8. Mauricio Velasquez, 2016. "Compositions vs Gini: A new metric to evaluate the effects of land-income disparities," 2016 Papers pve364, Job Market Papers.
    9. Defever, F. & Riaño, A., 2022. "Firm-Destination Heterogeneity and the Distribution of Export Intensity," Working Papers 22/01, Department of Economics, City University London.
    10. Janina Janurek & Sascha Abdel Hadi & Andreas Mojzisch & Jan Alexander Häusser, 2018. "The Association of the 24 Hour Distribution of Time Spent in Physical Activity, Work, and Sleep with Emotional Exhaustion," IJERPH, MDPI, vol. 15(9), pages 1-14, September.
    11. Quim Zaldo-Aubanell & Ferran Campillo i López & Albert Bach & Isabel Serra & Joan Olivet-Vila & Marc Saez & David Pino & Roser Maneja, 2021. "Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study," IJERPH, MDPI, vol. 18(7), pages 1-20, April.
    12. Monique Graf, 2020. "Regression for compositions based on a generalization of the Dirichlet distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 913-936, December.
    13. J. A. Martín-Fernández, 2021. "“Compositional Data Analysis in Practice” by Michael Greenacre Universitat Pompeu Fabra (Barcelona, Spain), Chapman and Hall/CRC, 2018," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 109-111, April.
    14. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
    15. Andriansyah, Andriansyah & Messinis, George, 2016. "Intended use of IPO proceeds and firm performance: A quantile regression approach," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 14-30.
    16. Charlotte Lund Rasmussen & Javier Palarea-Albaladejo & Mette Korshøj & Nidhi Gupta & Kirsten Nabe-Nielsen & Andreas Holtermann & Marie Birk Jørgensen, 2019. "Is high aerobic workload at work associated with leisure time physical activity and sedentary behaviour among blue-collar workers? A compositional data analysis based on accelerometer data," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.
    17. Morais, Joanna & Thomas-Agnan, Christine & Simioni, Michel, 2017. "Interpreting the impact of explanatory variables in compositional models," TSE Working Papers 17-805, Toulouse School of Economics (TSE).
    18. Dorothea Dumuid & Željko Pedišić & Javier Palarea-Albaladejo & Josep Antoni Martín-Fernández & Karel Hron & Timothy Olds, 2020. "Compositional Data Analysis in Time-Use Epidemiology: What, Why, How," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    19. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.
    20. Thomas-Agnan, Christine & Simioni, Michel & Trinh, Thi-Huong, 2023. "Discrete and Smooth Scalar-on-Density Compositional Regression for Assessing the Impact of Climate Change on Rice Yield in Vietnam," TSE Working Papers 23-1410, Toulouse School of Economics (TSE), revised Apr 2024.
    21. Tsagris, Michail, 2015. "Regression analysis with compositional data containing zero values," MPRA Paper 67868, University Library of Munich, Germany.
    22. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.
    23. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    24. Rieser, Christopher & Filzmoser, Peter, 2023. "Extending compositional data analysis from a graph signal processing perspective," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    25. Charlotte Lund Rasmussen & Javier Palarea-Albaladejo & Adrian Bauman & Nidhi Gupta & Kirsten Nabe-Nielsen & Marie Birk Jørgensen & Andreas Holtermann, 2018. "Does Physically Demanding Work Hinder a Physically Active Lifestyle in Low Socioeconomic Workers? A Compositional Data Analysis Based on Accelerometer Data," IJERPH, MDPI, vol. 15(7), pages 1-23, June.
    26. Jiajia Chen & Xiaoqin Zhang & Shengjia Li, 2017. "Multiple linear regression with compositional response and covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2270-2285, September.
    27. Mishra, Aditya & Müller, Christian L., 2022. "Robust regression with compositional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    28. Patrick L. Combettes & Christian L. Müller, 2021. "Regression Models for Compositional Data: General Log-Contrast Formulations, Proximal Optimization, and Microbiome Data Applications," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 217-242, July.
    29. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.

  18. Peter Filzmoser & Karel Hron & Matthias Templ, 2012. "Discriminant analysis for compositional data and robust parameter estimation," Computational Statistics, Springer, vol. 27(4), pages 585-604, December.

    Cited by:

    1. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.

  19. Neykov, N.M. & Čížek, P. & Filzmoser, P. & Neytchev, P.N., 2012. "The least trimmed quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1757-1770.

    Cited by:

    1. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
    2. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(1), pages 187-207, February.
    3. Yu-Yen Ku & Tze-Yu Yen, 2016. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-33, September.
    4. Umberto Nizza, 2023. "The expertise effect: the impact of legal specialists’ intervention on the timely delivery of laymen's judgments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 40(2), pages 589-614, July.
    5. Mafusalov, Alexander & Uryasev, Stan, 2016. "CVaR (superquantile) norm: Stochastic case," European Journal of Operational Research, Elsevier, vol. 249(1), pages 200-208.

  20. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.

    Cited by:

    1. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    2. Elena Catanese, 2016. "Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 101-117, June.
    3. Matthias Templ & Alexander Kowarik & Bernhard Meindl, 2014. "Development and Current Practice in Using R at Statistics Austria," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 173-184, June.
    4. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    5. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    6. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.

  21. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 37-56, April.

    Cited by:

    1. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    2. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    3. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
    4. Thiagarajah Ramilan & Shalander Kumar & Amare Haileslassie & Peter Craufurd & Frank Scrimgeour & Byjesh Kattarkandi & Anthony Whitbread, 2022. "Quantifying Farm Household Resilience and the Implications of Livelihood Heterogeneity in the Semi-Arid Tropics of India," Agriculture, MDPI, vol. 12(4), pages 1-15, March.
    5. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.

  22. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.

    Cited by:

    1. Beat Hulliger & Tobias Schoch, 2014. "Robust, distribution-free inference for income share ratios under complex sampling," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 63-85, January.
    2. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    3. Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).
    4. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    5. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
    6. Dana R. Thomson & Lieke Kools & Warren C. Jochem, 2018. "Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia," Data, MDPI, vol. 3(3), pages 1-19, August.
    7. Burgard Jan Pablo & Dieckmann Hanna & Krause Joscha & Merkle Hariolf & Münnich Ralf & Neufang Kristina M. & Schmaus Simon, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 191-211, August.
    8. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    9. Templ, Matthias & Kowarik, Alexander & Meindl, Bernhard, 2015. "Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i04).
    10. ANTONI Jean-Philippe & VUIDEL Gilles & KLEIN Olivier, 2017. "Generating a located synthetic population of individuals, households, and dwellings," LISER Working Paper Series 2017-07, Luxembourg Institute of Socio-Economic Research (LISER).
    11. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
    12. Amanda M. Y. Chu & Benson S. Y. Lam & Agnes Tiwari & Mike K. P. So, 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research," IJERPH, MDPI, vol. 16(22), pages 1-17, November.

  23. Templ, Matthias & Kowarik, Alexander & Filzmoser, Peter, 2011. "Iterative stepwise regression imputation using standard and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2793-2806, October.

    Cited by:

    1. Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    2. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 869-909, December.
    3. Pavlo Mozharovskyi & Julie Josse & François Husson, 2017. "Nonparametric imputation by data depth," Working Papers 2017-72, Center for Research in Economics and Statistics.
    4. Matthias Templ, 2023. "Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    5. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    6. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    7. Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
    8. Robbins Michael W., 2014. "The Utility of Nonparametric Transformations for Imputation of Survey Data," Journal of Official Statistics, Sciendo, vol. 30(4), pages 1-26, December.
    9. Frahm, Gabriel & Nordhausen, Klaus & Oja, Hannu, 2020. "M-estimation with incomplete and dependent multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    10. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    11. Arthur Stepchenko & Jurij Chizhov & Ludmila Aleksejeva, 2018. "Transfer of the data preprocessing parameters and fore- casting models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(6), pages 214-221.

  24. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.

    Cited by:

    1. Julie Poláčková & Andrea Jindrová, 2011. "Assessment of subjective aspects of the quality of life in the various regions of the Czech Republic," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(7), pages 267-274.
    2. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    3. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2013. "Robust distances for outlier-free goodness-of-fit testing," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 29-45.

  25. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.

    Cited by:

    1. Nikola Štefelová & Andreas Alfons & Javier Palarea-Albaladejo & Peter Filzmoser & Karel Hron, 2021. "Robust regression with compositional covariates including cellwise outliers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 869-909, December.
    2. M. A. Di Palma & M. Gallo, 2016. "A co-median approach to detect compositional outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(13), pages 2348-2362, October.
    3. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    4. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    5. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.
    6. Peter Filzmoser & Karel Hron & Matthias Templ, 2012. "Discriminant analysis for compositional data and robust parameter estimation," Computational Statistics, Springer, vol. 27(4), pages 585-604, December.
    7. Takahiro Yoshida & Morito Tsutsumi, 2018. "On the effects of spatial relationships in spatial compositional multivariate models," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 57-70, March.
    8. Hazen, Benjamin T. & Overstreet, Robert E. & Jones-Farmer, L. Allison & Field, Hubert S., 2012. "The role of ambiguity tolerance in consumer perception of remanufactured products," International Journal of Production Economics, Elsevier, vol. 135(2), pages 781-790.
    9. Elena Catanese, 2016. "Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 101-117, June.
    10. Maria Anna Di Palma & Michele Gallo, 2019. "External Information Model in a Compositional Perspective: Evaluation of Campania Adolescents’ Preferences in the Allocation of Leisure-Time," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 117-133, November.
    11. Templ, Matthias & Kowarik, Alexander & Filzmoser, Peter, 2011. "Iterative stepwise regression imputation using standard and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2793-2806, October.
    12. Garrido-Vega, Pedro & Ortega Jimenez, Cesar H. & de los Ríos, José Luis Díez Pérez & Morita, Michiya, 2015. "Implementation of technology and production strategy practices: Relationship levels in different industries," International Journal of Production Economics, Elsevier, vol. 161(C), pages 201-216.
    13. Martín-Fernández, J.A. & Hron, K. & Templ, M. & Filzmoser, P. & Palarea-Albaladejo, J., 2012. "Model-based replacement of rounded zeros in compositional data: Classical and robust approaches," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2688-2704.
    14. Frahm, Gabriel & Nordhausen, Klaus & Oja, Hannu, 2020. "M-estimation with incomplete and dependent multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    15. M. Templ & K. Hron & P. Filzmoser, 2017. "Exploratory tools for outlier detection in compositional data with structural zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 734-752, March.
    16. Mark A. Engle & Charles W. Nye & Ghanashyam Neupane & Scott A. Quillinan & Jonathan Fred McLaughlin & Travis McLing & Josep A. Martín-Fernández, 2022. "Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps," Energies, MDPI, vol. 15(13), pages 1-21, June.

  26. Todorov, Valentin & Filzmoser, Peter, 2010. "Robust statistic for the one-way MANOVA," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 37-48, January.

    Cited by:

    1. Sheng Lu, 2022. "Explore U.S. Retailers’ Sourcing Strategies for Clothing Made from Recycled Textile Materials," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    2. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
    3. Cerioli, Andrea & Farcomeni, Alessio, 2011. "Error rates for multivariate outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 544-553, January.

  27. Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).

    Cited by:

    1. Andrey Ziyatdinov & Alexandre Perera-Lluna, 2014. "Data Simulation in Machine Olfaction with the R Package Chemosensors," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-19, February.
    2. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    3. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
    4. Antonio Arcos & Maria del Mar Rueda & Sara Pasadas-del-Amo, 2020. "Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
    5. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    6. Zimmermann Thomas & Münnich Ralf Thomas, 2018. "Small Area Estimation with a Lognormal Mixed Model under Informative Sampling," Journal of Official Statistics, Sciendo, vol. 34(2), pages 523-542, June.
    7. Kreutzmann, Ann-Kristin & Pannier, Sören & Rojas-Perilla, Natalia & Schmid, Timo & Templ, Matthias & Tzavidis, Nikos, 2017. "The R package emdi for estimating and mapping regionally disaggregated indicators," Discussion Papers 2017/15, Free University Berlin, School of Business & Economics.
    8. Hofert, Marius & Mächler, Martin, 2016. "Parallel and Other Simulations in R Made Easy: An End-to-End Study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i04).
    9. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    10. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.

  28. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).

    Cited by:

    1. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    2. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    3. Francesca Torti & Aldo Corbellini & Anthony C. Atkinson, 2021. "fsdaSAS: A Package for Robust Regression for Very Large Datasets Including the Batch Forward Search," Stats, MDPI, vol. 4(2), pages 1-21, April.
    4. Kris Boudt & Valentin Todorov & Wenjing Wang, 2020. "Robust Distribution-Based Winsorization in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 375-397, June.
    5. Mara Velina & Janis Valeinis & Luca Greco & George Luta, 2016. "Empirical Likelihood-Based ANOVA for Trimmed Means," IJERPH, MDPI, vol. 13(10), pages 1-13, September.
    6. M. A. Di Palma & M. Gallo, 2016. "A co-median approach to detect compositional outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(13), pages 2348-2362, October.
    7. Sven Serneels, 2019. "Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis," Papers 1908.00141, arXiv.org.
    8. Rafael Laboissière & Pierre-Alain Barraud & Corinne Cian, 2017. "Real and visually-induced body inclination differently affect the perception of object stability," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-15, October.
    9. Langworthy, Benjamin W. & Stephens, Rebecca L. & Gilmore, John H. & Fine, Jason P., 2021. "Canonical correlation analysis for elliptical copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    10. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    11. Vilijandas Bagdonavičius & Linas Petkevičius, 2020. "A new multiple outliers identification method in linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 275-296, April.
    12. Dürre, Alexander & Vogel, Daniel & Fried, Roland, 2015. "Spatial sign correlation," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 89-105.
    13. Fiaschi, Davide & Gianmoena, Lisa & Parenti, Angela, 2018. "Spatial club dynamics in European regions," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 115-130.
    14. Leung, Andy & Zhang, Hongyang & Zamar, Ruben, 2016. "Robust regression estimation and inference in the presence of cellwise and casewise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 1-11.
    15. Katherine Morris & Paul McNicholas & Luca Scrucca, 2013. "Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 321-338, September.
    16. Steffen Liebscher & Thomas Kirschstein, 2015. "Efficiency of the pMST and RDELA location and scatter estimators," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 63-82, January.
    17. Aurea Grané & Rosario Romera, 2018. "On Visualizing Mixed-Type Data," Sociological Methods & Research, , vol. 47(2), pages 207-239, March.
    18. Henry Velasco & Henry Laniado & Mauricio Toro & Víctor Leiva & Yuhlong Lio, 2020. "Robust Three-Step Regression Based on Comedian and Its Performance in Cell-Wise and Case-Wise Outliers," Mathematics, MDPI, vol. 8(8), pages 1-18, August.
    19. Torti, Francesca & Corbellini, Aldo & Atkinson, Anthony C., 2021. "fsdaSAS: a package for robust regression for very large datasets including the batch forward search," LSE Research Online Documents on Economics 109895, London School of Economics and Political Science, LSE Library.
    20. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 37-56, April.
    21. Archimbaud, Aurore & Nordhausen, Klaus & Ruiz-Gazen, Anne, 2018. "ICS for multivariate outlier detection with application to quality control," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 184-199.
    22. Marco Riani & Andrea Cerioli & Francesca Torti, 2014. "On consistency factors and efficiency of robust S-estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 356-387, June.
    23. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
    24. Gabriela V. Cohen Freue & Hernan Ortiz-Molina & Ruben H. Zamar, 2013. "A Natural Robustification of the Ordinary Instrumental Variables Estimator," Biometrics, The International Biometric Society, vol. 69(3), pages 641-650, September.
    25. Luigi Grossi & Fabrizio Laurini, 2020. "Robust asset allocation with conditional value at risk using the forward search," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 335-352, May.
    26. Vakili, Kaveh & Schmitt, Eric, 2014. "Finding multivariate outliers with FastPCS," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 54-66.
    27. Alashwali, Fatimah & Kent, John T., 2016. "The use of a common location measure in the invariant coordinate selection and projection pursuit," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 145-161.
    28. Matthias Kohl & Peter Ruckdeschel & Helmut Rieder, 2010. "Infinitesimally Robust estimation in general smoothly parametrized models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 333-354, August.
    29. Cevallos-Valdiviezo, Holger & Van Aelst, Stefan, 2019. "Fast computation of robust subspace estimators," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 171-185.
    30. Jan Kalina & Jan Tichavský, 2022. "The minimum weighted covariance determinant estimator for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 977-999, December.
    31. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2013. "Robust distances for outlier-free goodness-of-fit testing," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 29-45.
    32. Van Aelst, Stefan & Willems, Gert, 2013. "Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i03).
    33. B. Barış Alkan, 2016. "Robust Principal Component Analysis Based on Modified Minimum Covariance Determinant in the Presence of Outliers," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 85-94, September.
    34. Thomas Ortner & Peter Filzmoser & Maia Rohm & Sarka Brodinova & Christian Breiteneder, 2021. "Local projections for high-dimensional outlier detection," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 189-206, August.
    35. Bilodeau, Martin & Micheaux, Pierre Lafaye de & Mahdi, Smail, 2015. "The R Package groc for Generalized Regression on Orthogonal Components," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i01).
    36. Selcuk Korkmaz & Gokmen Zararsiz & Dincer Goksuluk, 2015. "MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    37. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.
    38. Gianna S. Monti & Peter Filzmoser & Roland C. Deutsch, 2018. "A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions," Risk Analysis, John Wiley & Sons, vol. 38(10), pages 2073-2086, October.
    39. Nordhausen, Klaus & Oja, Hannu, 2011. "Multivariate L1 Statistical Methods: The Package MNM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i05).
    40. Domingo Docampo & Lawrence Cram, 2015. "On the effects of institutional size in university classifications: the case of the Shanghai ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1325-1346, February.
    41. Linh H. Nghiem & Francis K. C. Hui & Samuel Müller & Alan H. Welsh, 2022. "Estimation of graphical models for skew continuous data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1811-1841, December.
    42. Sanjeena Subedi & Paul McNicholas, 2014. "Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 167-193, June.
    43. T. Kirschstein & Steffen Liebscher, 2019. "Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(7), pages 1336-1349, May.

  29. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.

    Cited by:

    1. Heewon Park & Teppei Shimamura & Satoru Miyano & Seiya Imoto, 2014. "Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
    2. Chung, Hee Cheol & Ahn, Jeongyoun, 2021. "Subspace rotations for high-dimensional outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    3. Alexander A. Aduenko & Anastasia P. Motrenko & Vadim V. Strijov, 2018. "Object selection in credit scoring using covariance matrix of parameters estimations," Annals of Operations Research, Springer, vol. 260(1), pages 3-21, January.
    4. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
    5. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
    6. Silvia Salini & Andrea Cerioli & Fabrizio Laurini & Marco Riani, 2016. "Reliable Robust Regression Diagnostics," International Statistical Review, International Statistical Institute, vol. 84(1), pages 99-127, April.
    7. D. Rosadi & P. Filzmoser, 2019. "Robust second-order least-squares estimation for regression models with autoregressive errors," Statistical Papers, Springer, vol. 60(1), pages 105-122, February.
    8. Bernard, Carole & Vanduffel, Steven, 2015. "A new approach to assessing model risk in high dimensions," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 166-178.
    9. Boente, Graciela & Pires, Ana M. & Rodrigues, Isabel M., 2010. "Detecting influential observations in principal components and common principal components," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2967-2975, December.
    10. Thomas P. Triebs & Subal C. Kumbhakar, 2018. "Management in production: from unobserved to observed," Journal of Productivity Analysis, Springer, vol. 49(2), pages 111-121, June.
    11. Robert Serfling & Satyaki Mazumder, 2013. "Computationally easy outlier detection via projection pursuit with finitely many directions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 447-461, June.
    12. Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-24, February.
    13. Francesca DE BATTISTI & Silvia SALINI, 2011. "Robust analysis of bibliometric data," Departmental Working Papers 2011-36, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    14. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
    15. Francesca De Battisti & Silvia Salini, 2013. "Robust analysis of bibliometric data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 269-283, June.
    16. Luis Alfonso Menéndez-García & Paulino José García-Nieto & Esperanza García-Gonzalo & Fernando Sánchez Lasheras & Laura Álvarez-de-Prado & Antonio Bernardo-Sánchez, 2023. "Method for the Detection of Functional Outliers Applied to Quality Monitoring Samples in the Vicinity of El Musel Seaport in the Metropolitan Area of Gijón (Northern Spain)," Mathematics, MDPI, vol. 11(12), pages 1-23, June.
    17. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    18. Šárka Brodinová & Peter Filzmoser & Thomas Ortner & Christian Breiteneder & Maia Rohm, 2019. "Robust and sparse k-means clustering for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 905-932, December.
    19. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 37-56, April.
    20. Rob J. Hyndman & Han Lin Shang, 2008. "Rainbow plots, Bagplots and Boxplots for Functional Data," Monash Econometrics and Business Statistics Working Papers 9/08, Monash University, Department of Econometrics and Business Statistics.
    21. Thomas Triebs & Subal C. Kumbhakar, 2012. "Management Practice in Production," ifo Working Paper Series 129, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    22. Alam, Md. Ashad & Calhoun, Vince D. & Wang, Yu-Ping, 2018. "Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 70-85.
    23. Cerioli, Andrea & Farcomeni, Alessio, 2011. "Error rates for multivariate outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 544-553, January.
    24. Junlong Zhao & Chao Liu & Lu Niu & Chenlei Leng, 2019. "Multiple influential point detection in high dimensional regression spaces," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 385-408, April.
    25. Jan Kalina & Jan Tichavský, 2022. "The minimum weighted covariance determinant estimator for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 977-999, December.
    26. Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.
    27. Van Aelst, S. & Vandervieren, E. & Willems, G., 2012. "A Stahel–Donoho estimator based on huberized outlyingness," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 531-542.
    28. Agnieszka Wnęk & Dawid Kudas & Premysl Stych, 2021. "National Level Land-Use Changes in Functional Urban Areas in Poland, Slovakia, and Czechia," Land, MDPI, vol. 10(1), pages 1-16, January.
    29. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.
    30. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2013. "Robust distances for outlier-free goodness-of-fit testing," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 29-45.
    31. Thomas Ortner & Peter Filzmoser & Maia Rohm & Sarka Brodinova & Christian Breiteneder, 2021. "Local projections for high-dimensional outlier detection," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 189-206, August.
    32. David E. Tyler & Frank Critchley & Lutz Dümbgen & Hannu Oja, 2009. "Invariant co‐ordinate selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 549-592, June.
    33. C. Chatzinakos & L. Pitsoulis & G. Zioutas, 2016. "Optimization techniques for robust multivariate location and scatter estimation," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1443-1460, May.
    34. Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
    35. Stefano Marchetti & Caterina Giusti & Nicola Salvati & Monica Pratesi, 2017. "Small area estimation based on M-quantile models in presence of outliers in auxiliary variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 531-555, November.
    36. Tarr, G. & Müller, S. & Weber, N.C., 2016. "Robust estimation of precision matrices under cellwise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 404-420.

  30. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.

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    1. Antonio Punzo & Paul. D. McNicholas, 2017. "Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 249-293, July.
    2. L. A. García-Escudero & A. Gordaliza & C. Matrán & A. Mayo-Iscar, 2018. "Comments on “The power of monitoring: how to make the most of a contaminated multivariate sample”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 605-608, December.
    3. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    4. Alessio Farcomeni & Luca Greco, 2015. "S-estimation of hidden Markov models," Computational Statistics, Springer, vol. 30(1), pages 57-80, March.
    5. García-Escudero, Luis Angel & Gordaliza, Alfonso & Greselin, Francesca & Ingrassia, Salvatore & Mayo-Iscar, Agustín, 2016. "The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 131-147.
    6. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
    7. Holland, E.P. & Burrow, J.F. & Dytham, C. & Aegerter, J.N., 2009. "Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics," Ecological Modelling, Elsevier, vol. 220(9), pages 1203-1217.
    8. Wan-Lun Wang & Tsung-I Lin, 2015. "Robust model-based clustering via mixtures of skew-t distributions with missing information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 423-445, December.
    9. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    10. Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2023. "Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 75-102, March.
    11. Song, Weixing & Yao, Weixin & Xing, Yanru, 2014. "Robust mixture regression model fitting by Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 128-137.
    12. Luca Greco & Antonio Lucadamo & Claudio Agostinelli, 2021. "Weighted likelihood latent class linear regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 711-746, June.
    13. L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2013. "Comments on: model-based clustering and classification with non-normal mixture distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 459-461, November.
    14. Andrea Cerioli & Domenico Perrotta, 2014. "Robust clustering around regression lines with high density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 5-26, March.
    15. María Gallegos & Gunter Ritter, 2009. "Trimming algorithms for clustering contaminated grouped data and their robustness," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(2), pages 135-167, September.
    16. Li, Xiongya & Bai, Xiuqin & Song, Weixing, 2017. "Robust mixture multivariate linear regression by multivariate Laplace distribution," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 32-39.
    17. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 785-822, September.
    18. Fortini Marco, 2020. "An Improved Fellegi-Sunter Framework for Probabilistic Record Linkage Between Large Data Sets," Journal of Official Statistics, Sciendo, vol. 36(4), pages 803-825, December.
    19. C. Ruwet & L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2013. "On the breakdown behavior of the TCLUST clustering procedure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 466-487, September.
    20. Pietro Coretto & Christian Hennig, 2010. "A simulation study to compare robust clustering methods based on mixtures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 111-135, September.
    21. Neykov, N.M. & Filzmoser, P. & Neytchev, P.N., 2012. "Robust joint modeling of mean and dispersion through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 34-48, January.
    22. Chun Yu & Weixin Yao & Guangren Yang, 2020. "A Selective Overview and Comparison of Robust Mixture Regression Estimators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 176-202, April.
    23. Šárka Brodinová & Peter Filzmoser & Thomas Ortner & Christian Breiteneder & Maia Rohm, 2019. "Robust and sparse k-means clustering for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 905-932, December.
    24. Andrea Cappozzo & Francesca Greselin & Thomas Brendan Murphy, 2020. "A robust approach to model-based classification based on trimming and constraints," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 327-354, June.
    25. Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
    26. Hu, Hao & Yao, Weixin & Wu, Yichao, 2017. "The robust EM-type algorithms for log-concave mixtures of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 14-26.
    27. Sajobi, Tolulope T. & Lix, Lisa M. & Dansu, Bolanle M. & Laverty, William & Li, Longhai, 2012. "Robust descriptive discriminant analysis for repeated measures data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2782-2794.
    28. Francesca Torti & Domenico Perrotta & Marco Riani & Andrea Cerioli, 2019. "Assessing trimming methodologies for clustering linear regression data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 227-257, March.
    29. Cappozzo, Andrea & Greselin, Francesca & Murphy, Thomas Brendan, 2021. "Robust variable selection for model-based learning in presence of adulteration," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    30. García-Escudero, L.A. & Gordaliza, A. & Mayo-Iscar, A. & San Martín, R., 2010. "Robust clusterwise linear regression through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3057-3069, December.
    31. Andrea Cappozzo & Luis Angel García Escudero & Francesca Greselin & Agustín Mayo-Iscar, 2021. "Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling," Stats, MDPI, vol. 4(3), pages 1-14, July.
    32. Luca Greco, 2022. "Robust fitting of mixtures of GLMs by weighted likelihood," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 25-48, March.
    33. Meng Li & Sijia Xiang & Weixin Yao, 2016. "Robust estimation of the number of components for mixtures of linear regression models," Computational Statistics, Springer, vol. 31(4), pages 1539-1555, December.
    34. Bai, Xiuqin & Yao, Weixin & Boyer, John E., 2012. "Robust fitting of mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2347-2359.
    35. Johnson, Lorne D. & Sakoulis, Georgios, 2008. "Maximizing equity market sector predictability in a Bayesian time-varying parameter model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3083-3106, February.
    36. Luis García-Escudero & Alfonso Gordaliza & Carlos Matrán & Agustín Mayo-Iscar, 2010. "A review of robust clustering methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 89-109, September.
    37. Gallegos, María Teresa & Ritter, Gunter, 2010. "Using combinatorial optimization in model-based trimmed clustering with cardinality constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 637-654, March.
    38. VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.
    39. Chalabi, Yohan / Y. & Wuertz, Diethelm, 2010. "Weighted trimmed likelihood estimator for GARCH models," MPRA Paper 26536, University Library of Munich, Germany.
    40. Fritz, Heinrich & García-Escudero, Luis A. & Mayo-Iscar, Agustín, 2013. "A fast algorithm for robust constrained clustering," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 124-136.
    41. L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2014. "A constrained robust proposal for mixture modeling avoiding spurious solutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 27-43, March.
    42. Shi, Jianhong & Chen, Kun & Song, Weixing, 2014. "Robust errors-in-variables linear regression via Laplace distribution," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 113-120.

  31. P. Filzmoser & R. Viertl, 2004. "Testing hypotheses with fuzzy data: The fuzzy p-value," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 21-29, February.

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    1. Abbas Parchami & S. Mahmoud Taheri & Reinhard Viertl & Mashaallah Mashinchi, 2018. "Minimax test for fuzzy hypotheses," Statistical Papers, Springer, vol. 59(4), pages 1623-1648, December.
    2. Muhammad Aslam, 2022. "Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology," Stats, MDPI, vol. 5(3), pages 1-11, August.
    3. Hsu, Bi-Min & Shu, Ming-Hung, 2008. "Fuzzy inference to assess manufacturing process capability with imprecise data," European Journal of Operational Research, Elsevier, vol. 186(2), pages 652-670, April.
    4. S. Taheri & G. Hesamian, 2013. "A generalization of the Wilcoxon signed-rank test and its applications," Statistical Papers, Springer, vol. 54(2), pages 457-470, May.
    5. Viertl, Reinhard, 2006. "Univariate statistical analysis with fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 133-147, November.
    6. Shima Yosefi & Mohsen Arefi & Mohammad Ghasem Akbari, 2016. "A new approach for testing fuzzy hypotheses based on likelihood ratio statistic," Statistical Papers, Springer, vol. 57(3), pages 665-688, September.
    7. Jung-Lin Hung & Cheng-Che Chen & Chun-Mei Lai, 2020. "Possibility Measure of Accepting Statistical Hypothesis," Mathematics, MDPI, vol. 8(4), pages 1-16, April.
    8. Lubiano, María Asunción & Montenegro, Manuel & Sinova, Beatriz & de la Rosa de Sáa, Sara & Gil, María Ángeles, 2016. "Hypothesis testing for means in connection with fuzzy rating scale-based data: algorithms and applications," European Journal of Operational Research, Elsevier, vol. 251(3), pages 918-929.
    9. Abbas Parchami & S. Taheri & Mashaallah Mashinchi, 2012. "Testing fuzzy hypotheses based on vague observations: a p-value approach," Statistical Papers, Springer, vol. 53(2), pages 469-484, May.
    10. Wu, Chien-Wei, 2009. "Decision-making in testing process performance with fuzzy data," European Journal of Operational Research, Elsevier, vol. 193(2), pages 499-509, March.
    11. Nataliya Chukhrova & Arne Johannssen, 2020. "Randomized versus non-randomized hypergeometric hypothesis testing with crisp and fuzzy hypotheses," Statistical Papers, Springer, vol. 61(6), pages 2605-2641, December.
    12. Abbas Parchami & S. Taheri & Mashaallah Mashinchi, 2010. "Fuzzy p-value in testing fuzzy hypotheses with crisp data," Statistical Papers, Springer, vol. 51(1), pages 209-226, January.

  32. Pison, Greet & Rousseeuw, Peter J. & Filzmoser, Peter & Croux, Christophe, 2003. "Robust factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 145-172, January.

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    1. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    2. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    3. Theodore Metaxas, 2012. "Urban Advantages and Disadvantages in Southeastern Europe: An Appreciation of Industrial Firms by Using Exploratory Factor Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 81-104.
    4. Croux, Christophe & Joossens, Kristel, 2005. "Influence of observations on the misclassification probability in quadratic discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 384-403, October.
    5. Ella Roelant & Stefan Aelst & Gert Willems, 2009. "The minimum weighted covariance determinant estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(2), pages 177-204, September.
    6. Kim, Hea-Jung, 2018. "Bayesian hierarchical robust factor analysis models for partially observed sample-selection data," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 65-82.
    7. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    8. Metaxas, Theodore & Duquenne, Marie Noelle, 2015. "Small and Medium Sized Firms’ Competitiveness and Territorial Characteristics by using a MLR approach," MPRA Paper 66848, University Library of Munich, Germany.
    9. Yang, Mingan & Dunson, David B. & Baird, Donna, 2010. "Semiparametric Bayes hierarchical models with mean and variance constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2172-2186, September.
    10. Atkinson, Anthony C. & Riani, Marco & Cerioli, Andrea, 2017. "Cluster detection and clustering with random start forward searches," LSE Research Online Documents on Economics 72291, London School of Economics and Political Science, LSE Library.
    11. Metaxas, Theodore & Kallioras, Dimitris, 2013. "Small and medium-sized firms' competitiveness and territorial characteristics/assets: The cases of Bari, Varna and Thessaloniki," MPRA Paper 52446, University Library of Munich, Germany.
    12. Yi-Hao Kao & Benjamin Van Roy, 2014. "Directed Principal Component Analysis," Operations Research, INFORMS, vol. 62(4), pages 957-972, August.
    13. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    14. Kim, Hyoung-Moon & Maadooliat, Mehdi & Arellano-Valle, Reinaldo B. & Genton, Marc G., 2016. "Skewed factor models using selection mechanisms," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 162-177.
    15. METAXAS, Theodore, 2011. "Territorial Assets And Firms’ Competitiveness In Southern Europe: Industrial Vs Commercial Firms Using Exploratory Factor Analysis," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 11(1).
    16. Théodore METAXAS & Marie-Noëlle DUQUENNE, 2017. "Partnerships and development policies for small-medium enterprises in Greece: a CFA approach," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 45, pages 131-158.
    17. Alper Sinan & B. Barıs Alkan, 2015. "A useful approach to identify the multicollinearity in the presence of outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 986-993, May.
    18. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    19. Aleš Toman, 2014. "Robust confirmatory factor analysis based on the forward search algorithm," Statistical Papers, Springer, vol. 55(1), pages 233-252, February.
    20. Deliang Dai, 2020. "Mahalanobis Distances on Factor Model Based Estimation," Econometrics, MDPI, vol. 8(1), pages 1-11, March.
    21. Verdonck, T. & Debruyne, M., 2011. "The influence of individual claims on the chain-ladder estimates: Analysis and diagnostic tool," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 85-98, January.
    22. Cator, Eric A. & Lopuhaä, Hendrik P., 2010. "Asymptotic expansion of the minimum covariance determinant estimators," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2372-2388, November.
    23. Unkel, S. & Trendafilov, N.T., 2010. "A majorization algorithm for simultaneous parameter estimation in robust exploratory factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3348-3358, December.
    24. Christmann, A. & Van Aelst, S., 2006. "Robust estimation of Cronbach's alpha," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1660-1674, August.
    25. Angela Montanari & Cinzia Viroli, 2010. "A skew-normal factor model for the analysis of student satisfaction towards university courses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(3), pages 473-487.
    26. Pengfei Zhao & Lingxiang Wei & Dong Pan & Jincheng Yang & Yuchuan Ji, 2023. "Analysis of Key Factors Affecting Low-Carbon Travel Behaviors of Urban Residents in Developing Countries: A Case Study in Zhenjiang, China," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    27. Dupuis Lozeron, E. & Victoria-Feser, M.P., 2010. "Robust estimation of constrained covariance matrices for confirmatory factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3020-3032, December.

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