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Model selection and estimation in regression with grouped variables

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  1. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
  2. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers 56/15, Institute for Fiscal Studies.
  3. Naveen Naidu Narisetty, 2020. "Discussion," International Statistical Review, International Statistical Institute, vol. 88(2), pages 330-334, August.
  4. Aramayis Dallakyan, 2021. "Nonparanormal Structural VAR for Non-Gaussian Data," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1093-1113, April.
  5. Kwang Woo Ahn & Anjishnu Banerjee & Natasha Sahr & Soyoung Kim, 2018. "Group and within-group variable selection for competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 407-424, July.
  6. De Canditiis, Daniela & De Feis, Italia, 2019. "Simultaneous nonparametric regression in RADWT dictionaries," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 36-57.
  7. Lu, Xun & Su, Liangjun, 2016. "Shrinkage estimation of dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 190(1), pages 148-175.
  8. Du, Pang & Cheng, Guang & Liang, Hua, 2012. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2006-2017.
  9. Juntao Wang & Yuan Li, 2023. "DINA Model with Entropy Penalization," Mathematics, MDPI, vol. 11(18), pages 1-16, September.
  10. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
  11. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
  12. Anwen Yin, 2022. "Does the kitchen‐sink model work forecasting the equity premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 223-247, March.
  13. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Journal of Econometrics, Elsevier, vol. 208(2), pages 418-441.
  14. Faisal Zahid & Gerhard Tutz, 2013. "Multinomial logit models with implicit variable selection," 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(4), pages 393-416, December.
  15. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
  16. Patrick Breheny, 2015. "The group exponential lasso for bi‐level variable selection," Biometrics, The International Biometric Society, vol. 71(3), pages 731-740, September.
  17. Zhang, Yan-Qing & Tian, Guo-Liang & Tang, Nian-Sheng, 2016. "Latent variable selection in structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 190-205.
  18. Ziqi Chen & Chenlei Leng, 2016. "Dynamic Covariance Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1196-1207, July.
  19. Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
  20. Liu, Shu & You, Jinhong & Lian, Heng, 2017. "Estimation and model identification of longitudinal data time-varying nonparametric models," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 116-136.
  21. Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
  22. Rahul Ghosal & Arnab Maity & Timothy Clark & Stefano B. Longo, 2020. "Variable selection in functional linear concurrent regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 565-587, June.
  23. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
  24. Tung Duy Luu & Jalal Fadili & Christophe Chesneau, 2021. "Sampling from Non-smooth Distributions Through Langevin Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1173-1201, December.
  25. He, Yong & Zhang, Liang & Ji, Jiadong & Zhang, Xinsheng, 2019. "Robust feature screening for elliptical copula regression model," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 568-582.
  26. Karim Lounici & Massimiliano Pontil & Alexandre B. Tsybakov & Sara Van De Geer, 2010. "Oracle Inequalities and Optimal Inference under Group Sparsity," Working Papers 2010-35, Center for Research in Economics and Statistics.
  27. Lin S. Chen & Ross L. Prentice & Pei Wang, 2014. "A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation," Biometrics, The International Biometric Society, vol. 70(2), pages 312-322, June.
  28. Murat Genç & M. Revan Özkale, 2021. "Usage of the GO estimator in high dimensional linear models," Computational Statistics, Springer, vol. 36(1), pages 217-239, March.
  29. Fang, Xiaolei & Paynabar, Kamran & Gebraeel, Nagi, 2017. "Multistream sensor fusion-based prognostics model for systems with single failure modes," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 322-331.
  30. Lingyu Zhang & Xu Geng & Zhiwei Qin & Hongjun Wang & Xiao Wang & Ying Zhang & Jian Liang & Guobin Wu & Xuan Song & Yunhai Wang, 2022. "Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
  31. Yanhang Zhang & Junxian Zhu & Jin Zhu & Xueqin Wang, 2023. "A Splicing Approach to Best Subset of Groups Selection," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 104-119, January.
  32. Yanfang Zhang & Chuanhua Wei & Xiaolin Liu, 2022. "Group Logistic Regression Models with l p,q Regularization," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
  33. HONDA, Toshio & 本田, 敏雄 & ING, Ching-Kang & WU, Wei-Ying, 2017. "Adaptively weighted group Lasso for semiparametric quantile regression models," Discussion Papers 2017-04, Graduate School of Economics, Hitotsubashi University.
  34. Zhaoxing Gao & Ruey S. Tsay, 2021. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Papers 2103.14626, arXiv.org.
  35. Caner, Mehmet, 2023. "Generalized linear models with structured sparsity estimators," Journal of Econometrics, Elsevier, vol. 236(2).
  36. Faisal Maqbool Zahid & Shakeela Ramzan & Shahla Faisal & Ijaz Hussain, 2019. "Gender based survival prediction models for heart failure patients: A case study in Pakistan," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-10, February.
  37. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
  38. Feng, Guohua & Gao, Jiti & Peng, Bin & Zhang, Xiaohui, 2017. "A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks," Journal of Econometrics, Elsevier, vol. 196(1), pages 68-82.
  39. Aki-Hiro Sato & Hideki Takayasu, 2013. "Segmentation procedure based on Fisher's exact test and its application to foreign exchange rates," Papers 1309.0602, arXiv.org.
  40. Jun Yan & Jian Huang, 2012. "Model Selection for Cox Models with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 68(2), pages 419-428, June.
  41. Nanshan, Muye & Zhang, Nan & Xun, Xiaolei & Cao, Jiguo, 2022. "Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  42. Ye, Ya-Fen & Shao, Yuan-Hai & Deng, Nai-Yang & Li, Chun-Na & Hua, Xiang-Yu, 2017. "Robust Lp-norm least squares support vector regression with feature selection," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 32-52.
  43. Storlie, Curtis B. & Reich, Brian J. & Helton, Jon C. & Swiler, Laura P. & Sallaberry, Cedric J., 2013. "Analysis of computationally demanding models with continuous and categorical inputs," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 30-41.
  44. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
  45. Mingrui Zhong & Zanhua Yin & Zhichao Wang, 2023. "Variable Selection for Sparse Logistic Regression with Grouped Variables," Mathematics, MDPI, vol. 11(24), pages 1-21, December.
  46. Zhao, Junlong & Niu, Lu & Zhan, Shushi, 2017. "Trace regression model with simultaneously low rank and row(column) sparse parameter," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 1-18.
  47. Karsten Schweikert, 2022. "Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors," Papers 2201.05430, arXiv.org, revised Aug 2023.
  48. Ismail Shah & Hina Naz & Sajid Ali & Amani Almohaimeed & Showkat Ahmad Lone, 2023. "A New Quantile-Based Approach for LASSO Estimation," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
  49. Li Yun & O’Connor George T. & Dupuis Josée & Kolaczyk Eric, 2015. "Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 265-277, June.
  50. Sai Wang & Bin-Yuan Wang & Hai-Fang Li, 2023. "A Novel Meta-Analysis-Based Regularized Orthogonal Matching Pursuit Algorithm to Predict Lung Cancer with Selected Biomarkers," Mathematics, MDPI, vol. 11(19), pages 1-13, October.
  51. Hess, Wolfgang & Persson, Maria & Rubenbauer, Stephanie & Gertheiss, Jan, 2013. "Using Lasso-Type Penalties to Model Time-Varying Covariate Effects in Panel Data Regressions – A Novel Approach Illustrated by the ‘Death of Distance’ in International Trade," Working Paper Series 961, Research Institute of Industrial Economics.
  52. Guillaume Sagnol & Edouard Pauwels, 2019. "An unexpected connection between Bayes A-optimal designs and the group lasso," Statistical Papers, Springer, vol. 60(2), pages 565-584, April.
  53. Xinyu Kang & Apratim Ganguly & Eric D. Kolaczyk, 2022. "Dynamic Networks with Multi-scale Temporal Structure," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 218-260, June.
  54. Berriel, Tiago & Medeiros, Marcelo C. & Sena, Marcelo J., 2016. "Instrument selection for estimation of a forward-looking Phillips Curve," Economics Letters, Elsevier, vol. 145(C), pages 123-125.
  55. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
  56. Daye, Z. John & Jeng, X. Jessie, 2009. "Shrinkage and model selection with correlated variables via weighted fusion," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1284-1298, February.
  57. Khai X. Chiong & Hyungsik Roger Moon, 2017. "Estimation of Graphical Models using the $L_{1,2}$ Norm," Papers 1709.10038, arXiv.org, revised Oct 2017.
  58. Zhihua Sun & Yi Liu & Kani Chen & Gang Li, 2022. "Broken adaptive ridge regression for right-censored survival data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 69-91, February.
  59. Zangdong He & Wanzhu Tu & Sijian Wang & Haoda Fu & Zhangsheng Yu, 2015. "Simultaneous variable selection for joint models of longitudinal and survival outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 178-187, March.
  60. Lee, Sangin & Pawitan, Yudi & Lee, Youngjo, 2015. "A random-effect model approach for group variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 147-157.
  61. Hanwen Huang, 2017. "Controlling the false discoveries in LASSO," Biometrics, The International Biometric Society, vol. 73(4), pages 1102-1110, December.
  62. Xie Xiaodong & Zheng Shaozhi, 2017. "Group MCP for Cox Models with Time-Varying Coefficients," Journal of Systems Science and Information, De Gruyter, vol. 4(5), pages 476-488, October.
  63. Guan Yu & Yufeng Liu, 2016. "Sparse Regression Incorporating Graphical Structure Among Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 707-720, April.
  64. Yen-Shiu Chin & Ting-Li Chen, 2016. "Minimizing variable selection criteria by Markov chain Monte Carlo," Computational Statistics, Springer, vol. 31(4), pages 1263-1286, December.
  65. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
  66. Caiya Zhang & Yanbiao Xiang, 2016. "On the oracle property of adaptive group Lasso in high-dimensional linear models," Statistical Papers, Springer, vol. 57(1), pages 249-265, March.
  67. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
  68. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
  69. Charles‐Elie Rabier & Simona Grusea, 2021. "Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1001-1026, August.
  70. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(3), pages 923-941, October.
  71. Lee, Kyu Ha & Chakraborty, Sounak & Sun, Jianguo, 2017. "Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 1-13.
  72. Bin Luo & Xiaoli Gao, 2022. "A high-dimensional M-estimator framework for bi-level variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 559-579, June.
  73. Guojun Gan, 2018. "Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions," Risks, MDPI, vol. 6(3), pages 1-19, July.
  74. Skripnikov, A. & Michailidis, G., 2019. "Regularized joint estimation of related vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 164-177.
  75. Power, Michael Declan & Dong, Yuexiao, 2021. "Bayesian model averaging sliced inverse regression," Statistics & Probability Letters, Elsevier, vol. 174(C).
  76. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
  77. Gerda Claeskens, 2012. "Focused estimation and model averaging with penalization methods: an overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 272-287, August.
  78. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
  79. J. Kenneth Tay & Robert Tibshirani, 2020. "Reluctant Generalised Additive Modelling," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 205-224, December.
  80. Qiu, Debin & Ahn, Jeongyoun, 2020. "Grouped variable screening for ultra-high dimensional data for linear model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  81. Siwei Xia & Yuehan Yang & Hu Yang, 2022. "Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 255-277, March.
  82. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.
  83. Wei Lan & Ronghua Luo & Chih-Ling Tsai & Hansheng Wang & Yunhong Yang, 2015. "Testing the Diagonality of a Large Covariance Matrix in a Regression Setting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 76-86, January.
  84. Xiaoyi Yang & Nynke M. D. Niezink & Rebecca Nugent, 2021. "Learning social networks from text data using covariate information," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1399-1423, December.
  85. Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  86. Patric Müller & Sara Geer, 2016. "Censored linear model in high dimensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 75-92, March.
  87. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
  88. Peng, Heng & Lu, Ying, 2012. "Model selection in linear mixed effect models," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 109-129.
  89. Xia, Xiaochao & Yang, Hu & Li, Jialiang, 2016. "Feature screening for generalized varying coefficient models with application to dichotomous responses," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 85-97.
  90. Samuel Vaiter & Charles Deledalle & Jalal Fadili & Gabriel Peyré & Charles Dossal, 2017. "The degrees of freedom of partly smooth regularizers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 791-832, August.
  91. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
  92. G. Aneiros & P. Vieu, 2016. "Sparse nonparametric model for regression with functional covariate," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(4), pages 839-859, October.
  93. Warnke, Arne Jonas, 2017. "An investigation of record linkage refusal and its implications for empirical research," ZEW Discussion Papers 17-031, ZEW - Leibniz Centre for European Economic Research.
  94. Hui, Francis K.C. & Müller, Samuel & Welsh, A.H., 2020. "The LASSO on latent indices for regression modeling with ordinal categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
  95. Hai-Bin Zhang & Jiao-Jiao Jiang & Yun-Bin Zhao, 2015. "On the proximal Landweber Newton method for a class of nonsmooth convex problems," Computational Optimization and Applications, Springer, vol. 61(1), pages 79-99, May.
  96. Wei, Fengrong & Zhu, Hongxiao, 2012. "Group coordinate descent algorithms for nonconvex penalized regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 316-326.
  97. Ngai Hang Chan & Linhao Gao & Wilfredo Palma, 2022. "Simultaneous variable selection and structural identification for time‐varying coefficient models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 511-531, July.
  98. Tan Guo & Lei Zhang & Xiaoheng Tan & Liu Yang & Zhiwei Guo & Fupeng Wei, 2019. "CoLR: Classification-Oriented Local Representation for Image Recognition," Complexity, Hindawi, vol. 2019, pages 1-17, June.
  99. Benhuai Xie & Wei Pan & Xiaotong Shen, 2008. "Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters," Biometrics, The International Biometric Society, vol. 64(3), pages 921-930, September.
  100. Artem Sokolov & Daniel E Carlin & Evan O Paull & Robert Baertsch & Joshua M Stuart, 2016. "Pathway-Based Genomics Prediction using Generalized Elastic Net," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
  101. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
  102. Kaito Shimamura & Shuichi Kawano, 2021. "Bayesian sparse convex clustering via global-local shrinkage priors," Computational Statistics, Springer, vol. 36(4), pages 2671-2699, December.
  103. Weihua Zhao & Weiping Zhang & Heng Lian, 2020. "Marginal quantile regression for varying coefficient models with longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 213-234, February.
  104. Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo & M. Remedios Sillero-Denamiel, 2021. "A cost-sensitive constrained Lasso," 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(1), pages 121-158, March.
  105. Naoki Hamada & Shunsuke Ichiki, 2022. "Free Disposal Hull Condition to Verify When Efficiency Coincides with Weak Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 192(1), pages 248-270, January.
  106. Sunghoon Kwon & Jeongyoun Ahn & Woncheol Jang & Sangin Lee & Yongdai Kim, 2017. "A doubly sparse approach for group variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 997-1025, October.
  107. Luu, Tung Duy & Fadili, Jalal & Chesneau, Christophe, 2019. "PAC-Bayesian risk bounds for group-analysis sparse regression by exponential weighting," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 209-233.
  108. Jiehuan Sun & Jose D. Herazo‐Maya & Philip L. Molyneaux & Toby M. Maher & Naftali Kaminski & Hongyu Zhao, 2019. "Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome," Biometrics, The International Biometric Society, vol. 75(1), pages 69-77, March.
  109. Jared D. Huling & Menggang Yu & Muxuan Liang & Maureen Smith, 2018. "Risk prediction for heterogeneous populations with application to hospital admission prediction," Biometrics, The International Biometric Society, vol. 74(2), pages 557-565, June.
  110. Mohit Agrawal & Joseph G. Altonji & Richard K. Mansfield, 2019. "Quantifying Family, School, and Location Effects in the Presence of Complementarities and Sorting," Journal of Labor Economics, University of Chicago Press, vol. 37(S1), pages 11-83.
  111. Su, Liangjun & Ju, Gaosheng, 2018. "Identifying latent grouped patterns in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 554-573.
  112. Yan, Xiaodong & Wang, Hongni & Wang, Wei & Xie, Jinhan & Ren, Yanyan & Wang, Xinjun, 2021. "Optimal model averaging forecasting in high-dimensional survival analysis," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1147-1155.
  113. Piotr Swierkowski & Adrian Barnett, 2018. "Identification of hospital cost drivers using sparse group lasso," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
  114. G. Yi & J. Q. Shi & T. Choi, 2011. "Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1285-1294, December.
  115. Benjamin G. Stokell & Rajen D. Shah & Ryan J. Tibshirani, 2021. "Modelling high‐dimensional categorical data using nonconvex fusion penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 579-611, July.
  116. Aaron J. Molstad & Keshav Motwani, 2023. "Multiresolution categorical regression for interpretable cell‐type annotation," Biometrics, The International Biometric Society, vol. 79(4), pages 3485-3496, December.
  117. T. Rajala & D. J. Murrell & S. C. Olhede, 2018. "Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1237-1273, November.
  118. Fei Jin & Lung-fei Lee, 2018. "Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices," Econometrics, MDPI, vol. 6(1), pages 1-24, February.
  119. Christian Kanzow & Theresa Lechner, 2021. "Globalized inexact proximal Newton-type methods for nonconvex composite functions," Computational Optimization and Applications, Springer, vol. 78(2), pages 377-410, March.
  120. Dewei Zhang & Yin Liu & Sam Davanloo Tajbakhsh, 2022. "A First-Order Optimization Algorithm for Statistical Learning with Hierarchical Sparsity Structure," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1126-1140, March.
  121. Cui, Qiurong & Xu, Yuqing & Zhang, Zhengjun & Chan, Vincent, 2021. "Max-linear regression models with regularization," Journal of Econometrics, Elsevier, vol. 222(1), pages 579-600.
  122. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
  123. R. Lopes & S. A. Santos & P. J. S. Silva, 2019. "Accelerating block coordinate descent methods with identification strategies," Computational Optimization and Applications, Springer, vol. 72(3), pages 609-640, April.
  124. Toshio Honda & Wolfgang Karl Härdle, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers SFB649DP2012-061, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  125. He Jiang, 2023. "Robust forecasting in spatial autoregressive model with total variation regularization," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 195-211, March.
  126. Qing Zhou & Seunghyun Min, 2017. "Uncertainty quantification under group sparsity," Biometrika, Biometrika Trust, vol. 104(3), pages 613-632.
  127. Eduardo F. Mendes & Gabriel J. P. Pinto, 2023. "Generalized Information Criteria for Structured Sparse Models," Papers 2309.01764, arXiv.org.
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