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SparseNet: Coordinate Descent With Nonconvex Penalties

Citations

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Cited by:

  1. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
  2. 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.
  3. Jin Liu & Shuangge Ma & Jian Huang, 2014. "Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 87-103, March.
  4. Liu, Wenchen & Tang, Yincai & Wu, Xianyi, 2020. "Separating variables to accelerate non-convex regularized optimization," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
  5. VÁZQUEZ-ALCOCER, Alan & SCHOEN, Eric D. & GOOS, Peter, 2018. "A mixed integer optimization approach for model selection in screening experiments," Working Papers 2018007, University of Antwerp, Faculty of Business and Economics.
  6. Anda Tang & Pei Quan & Lingfeng Niu & Yong Shi, 2022. "A Survey for Sparse Regularization Based Compression Methods," Annals of Data Science, Springer, vol. 9(4), pages 695-722, August.
  7. Hu, Jianwei & Chai, Hao, 2013. "Adjusted regularized estimation in the accelerated failure time model with high dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 96-114.
  8. 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.
  9. 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.
  10. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
  11. Po-Hsien Huang & Hung Chen & Li-Jen Weng, 2017. "A Penalized Likelihood Method for Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 329-354, June.
  12. Kei Hirose & Miyuki Imada, 2018. "Sparse factor regression via penalized maximum likelihood estimation," Statistical Papers, Springer, vol. 59(2), pages 633-662, June.
  13. Nicholas G. Polson & James G. Scott, 2016. "Mixtures, envelopes and hierarchical duality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 701-727, September.
  14. Wei Sun & Yufeng Liu & James J. Crowley & Ting-Huei Chen & Hua Zhou & Haitao Chu & Shunping Huang & Pei-Fen Kuan & Yuan Li & Darla Miller & Ginger Shaw & Yichao Wu & Vasyl Zhabotynsky & Leonard McMill, 2015. "IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 975-986, September.
  15. Peili Li & Min Liu & Zhou Yu, 2023. "A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems," Computational Statistics, Springer, vol. 38(2), pages 871-898, June.
  16. Xinyi Liu & Gabriel Wallin & Yunxiao Chen & Irini Moustaki, 2023. "Rotation to Sparse Loadings Using $$L^p$$ L p Losses and Related Inference Problems," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 527-553, June.
  17. Yuta Umezu & Yusuke Shimizu & Hiroki Masuda & Yoshiyuki Ninomiya, 2019. "AIC for the non-concave penalized likelihood method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 247-274, April.
  18. Ben-Ameur, Walid & Neto, José, 2022. "New bounds for subset selection from conic relaxations," European Journal of Operational Research, Elsevier, vol. 298(2), pages 425-438.
  19. Yen, Yu-Min & Yen, Tso-Jung, 2014. "Solving norm constrained portfolio optimization via coordinate-wise descent algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 737-759.
  20. C. F. Jeff Wu, 2018. "A fresh look at effect aliasing and interactions: some new wine in old bottles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 249-268, April.
  21. He, Qianchuan & Kong, Linglong & Wang, Yanhua & Wang, Sijian & Chan, Timothy A. & Holland, Eric, 2016. "Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 222-239.
  22. Kohei Adachi & Nickolay T. Trendafilov, 2018. "Sparsest factor analysis for clustering variables: a matrix decomposition approach," 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 559-585, September.
  23. Shaobo Jin & Irini Moustaki & Fan Yang-Wallentin, 2018. "Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 628-649, September.
  24. Xiang Zhang & Yichao Wu & Lan Wang & Runze Li, 2016. "Variable selection for support vector machines in moderately high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 53-76, January.
  25. Dimitris Bertsimas & Angela King, 2016. "OR Forum—An Algorithmic Approach to Linear Regression," Operations Research, INFORMS, vol. 64(1), pages 2-16, February.
  26. 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.
  27. Wang, Yueyao & Lee, I-Chen & Hong, Yili & Deng, Xinwei, 2022. "Building degradation index with variable selection for multivariate sensory data," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
  28. 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.
  29. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
  30. Liu, Xinyi Lin & Wallin, Gabriel & Chen, Yunxiao & Moustaki, Irini, 2023. "Rotation to sparse loadings using Lp losses and related inference problems," LSE Research Online Documents on Economics 118349, London School of Economics and Political Science, LSE Library.
  31. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 267-286.
  32. Liqun Yu & Nan Lin, 2017. "ADMM for Penalized Quantile Regression in Big Data," International Statistical Review, International Statistical Institute, vol. 85(3), pages 494-518, December.
  33. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
  34. Carina Moreira Costa & Dennis Kreber & Martin Schmidt, 2022. "An Alternating Method for Cardinality-Constrained Optimization: A Computational Study for the Best Subset Selection and Sparse Portfolio Problems," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2968-2988, November.
  35. Piotr Pokarowski & Wojciech Rejchel & Agnieszka Sołtys & Michał Frej & Jan Mielniczuk, 2022. "Improving Lasso for model selection and prediction," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 831-863, June.
  36. Yingying Fan & Jinchi Lv, 2014. "Asymptotic properties for combined L1 and concave regularization," Biometrika, Biometrika Trust, vol. 101(1), pages 57-70.
  37. Michio Yamamoto & Heungsun Hwang, 2017. "Dimension-Reduced Clustering of Functional Data via Subspace Separation," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 294-326, July.
  38. Eun Ryung Lee & Hohsuk Noh & Byeong U. Park, 2014. "Model Selection via Bayesian Information Criterion for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 216-229, March.
  39. Runmin Shi & Faming Liang & Qifan Song & Ye Luo & Malay Ghosh, 2018. "A Blockwise Consistency Method for Parameter Estimation of Complex Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 179-223, December.
  40. Hirose, Kei & Tateishi, Shohei & Konishi, Sadanori, 2013. "Tuning parameter selection in sparse regression modeling," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 28-40.
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