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The group lasso for logistic regression

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Cited by:

  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. Honda, Toshio & Härdle, Wolfgang Karl, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers 2012-061, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  3. 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.
  4. Ma, Shuangge & Dai, Ying & Huang, Jian & Xie, Yang, 2012. "Identification of breast cancer prognosis markers via integrative analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2718-2728.
  5. 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.
  6. 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.
  7. 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.
  8. Caner, Mehmet, 2023. "Generalized linear models with structured sparsity estimators," Journal of Econometrics, Elsevier, vol. 236(2).
  9. Wang, Jimin & Ho, Choy Yeing (Chloe) & Shan, Yuan George, 2024. "Does cybersecurity risk stifle corporate innovation activities?," International Review of Financial Analysis, Elsevier, vol. 91(C).
  10. Yang, Yuan & McMahan, Christopher S. & Wang, Yu-Bo & Ouyang, Yuyuan, 2024. "Estimation of l0 norm penalized models: A statistical treatment," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  11. Sang, Yongli & Dang, Xin, 2024. "Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
  12. 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.
  13. Sanjana Gupta & Robin E C Lee & James R Faeder, 2020. "Parallel Tempering with Lasso for model reduction in systems biology," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-22, March.
  14. 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.
  15. Chen, Qi-an & Hu, Qingyu & Yang, Hu & Qi, Kai, 2022. "A kind of new time-weighted nonnegative lasso index-tracking model and its application," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
  16. 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.
  17. 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.
  18. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
  19. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
  20. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
  21. Beyhum, Jad & Portier, François, 2024. "High-dimensional nonconvex LASSO-type M-estimators," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
  22. 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.
  23. Wei, Fengrong & Zhu, Hongxiao, 2012. "Group coordinate descent algorithms for nonconvex penalized regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 316-326.
  24. Baiguo An & Beibei Zhang, 2020. "Logistic regression with image covariates via the combination of L1 and Sobolev regularizations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. Abhik Ghosh & Magne Thoresen, 2018. "Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 179-210, April.
  30. 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.
  31. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
  32. repec:hum:wpaper:sfb649dp2012-061 is not listed on IDEAS
  33. Huwe, Vera & Gessner, Johannes, 2020. "Are there rebound effects from electric vehicle adoption? Evidence from German household data," ZEW Discussion Papers 20-048, ZEW - Leibniz Centre for European Economic Research.
  34. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
  35. Yoon, Hyui Geon & Kim, Hyungjun & Kim, Chang Ouk & Song, Min, 2016. "Opinion polarity detection in Twitter data combining shrinkage regression and topic modeling," Journal of Informetrics, Elsevier, vol. 10(2), pages 634-644.
  36. Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024. "Online Distributional Regression," Papers 2407.08750, arXiv.org, revised Aug 2024.
  37. Abdallah Mkhadri & Mohamed Ouhourane, 2015. "A group VISA algorithm for variable selection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 41-60, March.
  38. Hendrik van der Wurp & Andreas Groll, 2023. "Introducing LASSO-type penalisation to generalised joint regression modelling for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 127-151, March.
  39. Shizhe Chen & Ali Shojaie & Daniela M. Witten, 2017. "Network Reconstruction From High-Dimensional Ordinary Differential Equations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1697-1707, October.
  40. Mohamed Ouhourane & Karim Oualkacha & Archer Yi Yang, 2024. "Group penalized expectile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1251-1313, November.
  41. Silvia Villa & Lorenzo Rosasco & Sofia Mosci & Alessandro Verri, 2014. "Proximal methods for the latent group lasso penalty," Computational Optimization and Applications, Springer, vol. 58(2), pages 381-407, June.
  42. Jin Zhang & Xide Zhu, 2022. "Linear Convergence of Prox-SVRG Method for Separable Non-smooth Convex Optimization Problems under Bounded Metric Subregularity," Journal of Optimization Theory and Applications, Springer, vol. 192(2), pages 564-597, February.
  43. He, Xin & Mao, Xiaojun & Wang, Zhonglei, 2024. "Nonparametric augmented probability weighting with sparsity," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
  44. Mingqiu Wang & Guo-Liang Tian, 2019. "Adaptive group Lasso for high-dimensional generalized linear models," Statistical Papers, Springer, vol. 60(5), pages 1469-1486, October.
  45. A. Poterie & J.-F. Dupuy & V. Monbet & L. Rouvière, 2019. "Classification tree algorithm for grouped variables," Computational Statistics, Springer, vol. 34(4), pages 1613-1648, December.
  46. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
  47. Gerhard Tutz & Gunther Schauberger, 2015. "A Penalty Approach to Differential Item Functioning in Rasch Models," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 21-43, March.
  48. A. Karagrigoriou & C. Koukouvinos & K. Mylona, 2010. "On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 13-24.
  49. Liu, Jianyu & Yu, Guan & Liu, Yufeng, 2019. "Graph-based sparse linear discriminant analysis for high-dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 250-269.
  50. Khishigsuren Davagdorj & Van Huy Pham & Nipon Theera-Umpon & Keun Ho Ryu, 2020. "XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction," IJERPH, MDPI, vol. 17(18), pages 1-22, September.
  51. Yang, Hu & Yi, Danhui, 2015. "Studies of the adaptive network-constrained linear regression and its application," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 40-52.
  52. Choi, Hosik & Yeo, Donghwa & Kwon, Sunghoon & Kim, Yongdai, 2011. "Gene selection and prediction for cancer classification using support vector machines with a reject option," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1897-1908, May.
  53. Lee, Wonyul & Liu, Yufeng, 2012. "Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 241-255.
  54. Chen, Shunjie & Yang, Sijia & Wang, Pei & Xue, Liugen, 2023. "Two-stage penalized algorithms via integrating prior information improve gene selection from omics data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
  55. Wu, Lan & Yang, Yuehan & Liu, Hanzhong, 2014. "Nonnegative-lasso and application in index tracking," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 116-126.
  56. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
  57. Kuangnan Fang & Xinyan Fan & Wei Lan & Bingquan Wang, 2019. "Nonparametric additive beta regression for fractional response with application to body fat data," Annals of Operations Research, Springer, vol. 276(1), pages 331-347, May.
  58. Simon Hirsch & Florian Ziel, 2022. "Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution," Papers 2211.13002, arXiv.org.
  59. Canhong Wen & Zhenduo Li & Ruipeng Dong & Yijin Ni & Wenliang Pan, 2023. "Simultaneous Dimension Reduction and Variable Selection for Multinomial Logistic Regression," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1044-1060, September.
  60. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
  61. Groll, Andreas & Hambuckers, Julien & Kneib, Thomas & Umlauf, Nikolaus, 2019. "LASSO-type penalization in the framework of generalized additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 140(C), pages 59-73.
  62. Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
  63. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
  64. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
  65. Liu, Xianhui & Wang, Zhanfeng & Wu, Yaohua, 2013. "Group variable selection and estimation in the tobit censored response model," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 80-89.
  66. Jing Qian & Seyedmehdi Payabvash & André Kemmling & Michael H. Lev & Lee H. Schwamm & Rebecca A. Betensky, 2014. "Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients," Biometrics, The International Biometric Society, vol. 70(1), pages 153-163, March.
  67. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions," CREATES Research Papers 2012-38, Department of Economics and Business Economics, Aarhus University.
  68. Dong, Manh Cuong & Tian, Shaonan & Chen, Cathy W.S., 2018. "Predicting failure risk using financial ratios: Quantile hazard model approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 204-220.
  69. Choi, Sungwoo & Park, Junyong, 2014. "Nonparametric additive model with grouped lasso and maximizing area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 313-325.
  70. Raja Chakir & Thibault Laurent & Anne Ruiz-Gazen & Christine Thomas-Agnan & Céline Vignes, 2017. "Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 435-469.
  71. Yang, Yanlin & Hu, Xuemei & Jiang, Huifeng, 2022. "Group penalized logistic regressions predict up and down trends for stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
  72. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
  73. Fan, Xianqiu & Cheng, Jun & Wang, Hailing & Zhang, Bin & Chen, Zhenzhen, 2024. "A fast trans-lasso algorithm with penalized weighted score function," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  74. Zeng, Yaohui & Yang, Tianbao & Breheny, Patrick, 2021. "Hybrid safe–strong rules for efficient optimization in lasso-type problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
  75. Yongxiu Cao & Jian Huang & Yanyan Liu & Xingqiu Zhao, 2016. "Sieve estimation of Cox models with latent structures," Biometrics, The International Biometric Society, vol. 72(4), pages 1086-1097, December.
  76. Faisal Maqbool Zahid & Gerhard Tutz, 2013. "Proportional Odds Models with High‐Dimensional Data Structure," International Statistical Review, International Statistical Institute, vol. 81(3), pages 388-406, December.
  77. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
  78. Kyoungjae Lee & Xuan Cao, 2021. "Bayesian group selection in logistic regression with application to MRI data analysis," Biometrics, The International Biometric Society, vol. 77(2), pages 391-400, June.
  79. Shota Yamanaka & Nobuo Yamashita, 2018. "Duality of nonconvex optimization with positively homogeneous functions," Computational Optimization and Applications, Springer, vol. 71(2), pages 435-456, November.
  80. Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2023. "A Review of Data‐Driven Discovery for Dynamic Systems," International Statistical Review, International Statistical Institute, vol. 91(3), pages 464-492, December.
  81. Lee, In Gyu & Yoon, Sang Won & Won, Daehan, 2022. "A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1055-1068.
  82. Luoying Yang & Tong Tong Wu, 2023. "Model‐based clustering of high‐dimensional longitudinal data via regularization," Biometrics, The International Biometric Society, vol. 79(2), pages 761-774, June.
  83. Laisong Kang & Shifeng Liu & Daqing Gong & Mincong Tang, 2021. "A personalized point-of-interest recommendation system for O2O commerce," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 253-267, June.
  84. Xuemin Gu & Nan Chen & Caimiao Wei & Suyu Liu & Vassiliki A. Papadimitrakopoulou & Roy S. Herbst & J. Jack Lee, 2016. "Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 99-128, June.
  85. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
  86. Ruidi Chen & Ioannis Ch. Paschalidis, 2022. "Robust Grouped Variable Selection Using Distributionally Robust Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1042-1071, September.
  87. Ciarleglio, Adam & Todd Ogden, R., 2016. "Wavelet-based scalar-on-function finite mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 86-96.
  88. Xian Zhang & Dingtao Peng, 2022. "Solving constrained nonsmooth group sparse optimization via group Capped- $$\ell _1$$ ℓ 1 relaxation and group smoothing proximal gradient algorithm," Computational Optimization and Applications, Springer, vol. 83(3), pages 801-844, December.
  89. Fabian Scheipl & Thomas Kneib & Ludwig Fahrmeir, 2013. "Penalized likelihood and Bayesian function selection in regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 349-385, October.
  90. Minh Pham & Xiaodong Lin & Andrzej Ruszczyński & Yu Du, 2021. "An outer–inner linearization method for non-convex and nondifferentiable composite regularization problems," Journal of Global Optimization, Springer, vol. 81(1), pages 179-202, September.
  91. Gabriel E Hoffman & Benjamin A Logsdon & Jason G Mezey, 2013. "PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-19, June.
  92. Bang, Sungwan & Jhun, Myoungshic, 2012. "Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 813-826.
  93. Wolfgang Hess & Maria Persson, 2012. "The duration of trade revisited," Empirical Economics, Springer, vol. 43(3), pages 1083-1107, December.
  94. Pierre Alquier & Vincent Cottet & Guillaume Lecué, 2017. "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions," Working Papers 2017-30, Center for Research in Economics and Statistics.
  95. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
  96. Baosheng Liang & Peng Wu & Xingwei Tong & Yanping Qiu, 2020. "Regression and subgroup detection for heterogeneous samples," Computational Statistics, Springer, vol. 35(4), pages 1853-1878, December.
  97. Matthew Hindman, 2015. "Building Better Models," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 48-62, May.
  98. Xianwen Ding & Zhihuang Yang, 2024. "Adaptive Bi-Level Variable Selection for Quantile Regression Models with a Diverging Number of Covariates," Mathematics, MDPI, vol. 12(20), pages 1-23, October.
  99. repec:jss:jstsof:33:i01 is not listed on IDEAS
  100. Li, Jingyu & Guo, Ce & Lv, Sijia & Xie, Qiwei & Zheng, Xiaolong, 2024. "Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter?," Emerging Markets Review, Elsevier, vol. 62(C).
  101. Bilin Zeng & Xuerong Meggie Wen & Lixing Zhu, 2017. "A link-free sparse group variable selection method for single-index model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2388-2400, October.
  102. Michele Lalla & Davide Ferrari & Patrizio Frederic, 2012. "Unit nonresponse errors in income surveys: a case study," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1769-1794, October.
  103. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
  104. Jenna Marie Reps & M Soledad Cepeda & Patrick B Ryan, 2020. "Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
  105. Jiyong Eom & Frank A. Wolak, 2020. "Breaking Routine for Energy Savings: An Appliance-level Analysis of Small Business Behavior under Dynamic Prices," NBER Working Papers 27263, National Bureau of Economic Research, Inc.
  106. Wanling Xie & Hu Yang, 2023. "Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 469-507, September.
  107. Young Joo Yoon & Cheolwoo Park & Erik Hofmeister & Sangwook Kang, 2012. "Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1605-1621, January.
  108. Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2021. "Conditional score matching for high-dimensional partial graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
  109. Lichun Wang & Yuan You & Heng Lian, 2015. "Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models," Statistical Papers, Springer, vol. 56(3), pages 819-828, August.
  110. Mingqiu Wang & Guo-Liang Tian, 2016. "Robust group non-convex estimations for high-dimensional partially linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 49-67, March.
  111. Luis M. Briceño-Arias & Giovanni Chierchia & Emilie Chouzenoux & Jean-Christophe Pesquet, 2019. "A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression," Computational Optimization and Applications, Springer, vol. 72(3), pages 707-726, April.
  112. Yang, Yuehan & Xia, Siwei & Yang, Hu, 2023. "Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  113. Haowen Bao & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2024. "Sparse Interval-valued Time Series Modeling with Machine Learning," Papers 2411.09452, arXiv.org.
  114. Matsui, Hidetoshi, 2014. "Variable and boundary selection for functional data via multiclass logistic regression modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 176-185.
  115. P. Tseng & S. Yun, 2009. "Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization," Journal of Optimization Theory and Applications, Springer, vol. 140(3), pages 513-535, March.
  116. Kaida Cai & Hua Shen & Xuewen Lu, 2022. "Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 968-993, December.
  117. Xiaoping Liu & Xiao-Bai Li & Sumit Sarkar, 2023. "Cost-Restricted Feature Selection for Data Acquisition," Management Science, INFORMS, vol. 69(7), pages 3976-3992, July.
  118. Ziqi Chen & Man-Lai Tang & Wei Gao & Ning-Zhong Shi, 2014. "New Robust Variable Selection Methods for Linear Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 725-741, September.
  119. Nicolas Städler & Peter Bühlmann & Sara Geer, 2010. "ℓ 1 -penalization for mixture regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 209-256, August.
  120. Olga Klopp & Marianna Pensky, 2013. "Sparse High-dimensional Varying Coefficient Model : Non-asymptotic Minimax Study," Working Papers 2013-30, Center for Research in Economics and Statistics.
  121. Pei Wang & Shunjie Chen & Sijia Yang, 2022. "Recent Advances on Penalized Regression Models for Biological Data," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
  122. Ilya O. Ryzhov & Bin Han & Jelena Bradić, 2016. "Cultivating Disaster Donors Using Data Analytics," Management Science, INFORMS, vol. 62(3), pages 849-866, March.
  123. A. Antoniadis & I. Gijbels & S. Lambert-Lacroix, 2014. "Penalized estimation in additive varying coefficient models using grouped regularization," Statistical Papers, Springer, vol. 55(3), pages 727-750, August.
  124. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
  125. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
  126. Hamsa Bastani, 2021. "Predicting with Proxies: Transfer Learning in High Dimension," Management Science, INFORMS, vol. 67(5), pages 2964-2984, May.
  127. Yuan Jiang & Yunxiao He & Heping Zhang, 2016. "Variable Selection With Prior Information for Generalized Linear Models via the Prior LASSO Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 355-376, March.
  128. Zanhua Yin, 2020. "Variable selection for sparse logistic regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 821-836, October.
  129. Ching-pei Lee & Stephen J. Wright, 2020. "Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 151-187, April.
  130. Xiaoya Zhang & Wei Peng & Hui Zhang, 2022. "Inertial proximal incremental aggregated gradient method with linear convergence guarantees," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 96(2), pages 187-213, October.
  131. Wenying Wu & Dingtao Peng, 2021. "Optimality Conditions for Group Sparse Constrained Optimization Problems," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
  132. Li, Peili & Jiao, Yuling & Lu, Xiliang & Kang, Lican, 2022. "A data-driven line search rule for support recovery in high-dimensional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  133. Haibin Zhang & Juan Wei & Meixia Li & Jie Zhou & Miantao Chao, 2014. "On proximal gradient method for the convex problems regularized with the group reproducing kernel norm," Journal of Global Optimization, Springer, vol. 58(1), pages 169-188, January.
  134. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.
  135. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
  136. Reher, Leonie & Runst, Petrik & Thomä, Jörg & Bizer, Kilian, 2024. "Measuring non-R&D drivers of innovation: The case of SMEs in lagging regions," ifh Working Papers 45/2024, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
  137. Osamu Komori & Shinto Eguchi & John B. Copas, 2015. "Generalized t-statistic for two-group classification," Biometrics, The International Biometric Society, vol. 71(2), pages 404-416, June.
  138. Fan Xia & Jun Chen & Wing Kam Fung & Hongzhe Li, 2013. "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis," Biometrics, The International Biometric Society, vol. 69(4), pages 1053-1063, December.
  139. Jian Huang & Yuling Jiao & Lican Kang & Jin Liu & Yanyan Liu & Xiliang Lu, 2022. "GSDAR: a fast Newton algorithm for $$\ell _0$$ ℓ 0 regularized generalized linear models with statistical guarantee," Computational Statistics, Springer, vol. 37(1), pages 507-533, March.
  140. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP092020, School of Economics, University of Queensland, Australia.
  141. Igor Konnov, 2017. "An Adaptive Partial Linearization Method for Optimization Problems on Product Sets," Journal of Optimization Theory and Applications, Springer, vol. 175(2), pages 478-501, November.
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