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Dimension reduction and coefficient estimation in multivariate linear regression

Citations

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Cited by:

  1. Marie Levakova & Susanne Ditlevsen, 2024. "Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models: A Review," International Statistical Review, International Statistical Institute, vol. 92(2), pages 160-193, August.
  2. Pan Shang & Lingchen Kong, 2021. "Regularization Parameter Selection for the Low Rank Matrix Recovery," Journal of Optimization Theory and Applications, Springer, vol. 189(3), pages 772-792, June.
  3. Zehua Chen & Yiwei Jiang, 2020. "A two-stage sequential conditional selection approach to sparse high-dimensional multivariate regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 65-90, February.
  4. Donghwi Nam & Ja-Yong Koo & Kwan-Young Bak, 2025. "Dimensionality reduction in multivariate nonparametric regression via nuclear norm penalization," Statistical Papers, Springer, vol. 66(3), pages 1-33, April.
  5. Yiting Ma & Pan Shang & Lingchen Kong, 2025. "Tuning parameter selection for the adaptive nuclear norm regularized trace regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(3), pages 491-516, June.
  6. Luwan Zhang & Grace Wahba & Ming Yuan, 2016. "Distance shrinkage and Euclidean embedding via regularized kernel estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 849-867, September.
  7. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
  8. Kargin, Vladislav, 2015. "On estimation in the reduced-rank regression with a large number of responses and predictors," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 377-394.
  9. Xiaohang Wang & Ling Xin & Philip L. H. Yu, 2025. "Matrix Autoregressive Time Series With Reduced‐Rank and Sparse Structural Constraints," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(8), pages 2442-2458, December.
  10. Kun Chen & Yanyuan Ma, 2017. "Analysis of Double Single Index Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 1-20, March.
  11. Chao, Shih-Kang & Härdle, Wolfgang K. & Huang, Chen, 2018. "Multivariate factorizable expectile regression with application to fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 1-19.
  12. Härdle, Wolfgang Karl & Huang, Chen & Chao, Shih-Kang, 2016. "Factorisable sparse tail event curves with expectiles," SFB 649 Discussion Papers 2016-018, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  13. Fujikoshi, Yasunori & Sakurai, Tetsuro, 2016. "High-dimensional consistency of rank estimation criteria in multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 199-212.
  14. Goh, Gyuhyeong & Dey, Dipak K. & Chen, Kun, 2017. "Bayesian sparse reduced rank multivariate regression," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 14-28.
  15. Tsukuda, Koji & Matsuura, Shun, 2025. "Estimators for multivariate allometric regression model," Journal of Multivariate Analysis, Elsevier, vol. 210(C).
  16. 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.
  17. Bamdev Mishra & Gilles Meyer & Silvère Bonnabel & Rodolphe Sepulchre, 2014. "Fixed-rank matrix factorizations and Riemannian low-rank optimization," Computational Statistics, Springer, vol. 29(3), pages 591-621, June.
  18. repec:hum:wpaper:sfb649dp2016-058 is not listed on IDEAS
  19. repec:hum:wpaper:sfb649dp2016-018 is not listed on IDEAS
  20. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2015. "Factorisable sparse tail event curves," SFB 649 Discussion Papers 2015-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  21. 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.
  22. Yoshio Takane & Sunho Jung, 2008. "Regularized Partial and/or Constrained Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 671-690, December.
  23. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  24. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
  25. An, Baiguo & Zhang, Beibei, 2017. "Simultaneous selection of predictors and responses for high dimensional multivariate linear regression," Statistics & Probability Letters, Elsevier, vol. 127(C), pages 173-177.
  26. Liang, Weixiong & Yang, Yuehan, 2026. "Iterative sequential screening strategies for sparse recovery with computational advantages," Journal of Multivariate Analysis, Elsevier, vol. 212(C).
  27. repec:hum:wpaper:sfb649dp2015-034 is not listed on IDEAS
  28. Ke, Yuan & Zhang, Rongmao & Zhang, Wenyang & Zou, Changliang, 2026. "Hypothesis test in high dimensional multi-response linear models," Computational Statistics & Data Analysis, Elsevier, vol. 215(C).
  29. Chao, Shih-Kang & Härdle, Wolfgang Karl & Huang, Chen, 2016. "Multivariate factorisable sparse asymmetric least squares regression," SFB 649 Discussion Papers 2016-058, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  30. Miyashiro, Ryuhei & Takano, Yuichi, 2015. "Mixed integer second-order cone programming formulations for variable selection in linear regression," European Journal of Operational Research, Elsevier, vol. 247(3), pages 721-731.
  31. Kohei Yoshikawa & Shuichi Kawano, 2023. "Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization," Computational Statistics, Springer, vol. 38(1), pages 53-75, March.
  32. Vladimir M. Cvetković & Neda Nikolić & Adem Ocal & Jovana Martinović & Aleksandar Dragašević, 2022. "A Predictive Model of Pandemic Disaster Fear Caused by Coronavirus (COVID-19): Implications for Decision-Makers," IJERPH, MDPI, vol. 19(2), pages 1-27, January.
  33. Mishra, Aditya & Dey, Dipak K. & Chen, Yong & Chen, Kun, 2021. "Generalized co-sparse factor regression," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
  34. Jin Liu & Jian Huang & Shuangge Ma, 2012. "Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-12, December.
  35. Changliang Zou & Xianghui Ning & Fugee Tsung, 2012. "LASSO-based multivariate linear profile monitoring," Annals of Operations Research, Springer, vol. 192(1), pages 3-19, January.
  36. Fanhua Shang & Yuanyuan Liu & Fanjie Shang & Hongying Liu & Lin Kong & Licheng Jiao, 2020. "A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
  37. Li, Mei & Kong, Lingchen, 2019. "Double fused Lasso penalized LAD for matrix regression," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 119-138.
  38. Kharratzadeh, Milad & Coates, Mark, 2017. "Semi-parametric order-based generalized multivariate regression," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 89-102.
  39. Arash Karimzadeh & Omidreza Shoghli & Sepehr Sabeti & Hamed Tabkhi, 2022. "Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
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