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Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

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  • Yanming Li
  • Bin Nan
  • Ji Zhu

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  • Yanming Li & Bin Nan & Ji Zhu, 2015. "Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure," Biometrics, The International Biometric Society, vol. 71(2), pages 354-363, June.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:354-363
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    File URL: http://hdl.handle.net/10.1111/biom.12292
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. P. Tseng, 2001. "Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization," Journal of Optimization Theory and Applications, Springer, vol. 109(3), pages 475-494, June.
    4. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    7. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    8. Swati Biswas & Shili Lin, 2012. "Logistic Bayesian LASSO for Identifying Association with Rare Haplotypes and Application to Age-Related Macular Degeneration," Biometrics, The International Biometric Society, vol. 68(2), pages 587-597, June.
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    3. Wang, Yihe & Zhao, Sihai Dave, 2021. "A nonparametric empirical Bayes approach to large-scale multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    4. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    5. 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.
    6. Yu-Zhu Tian & Man-Lai Tang & Mao-Zai Tian, 2021. "Bayesian joint inference for multivariate quantile regression model with L $$_{1/2}$$ 1 / 2 penalty," Computational Statistics, Springer, vol. 36(4), pages 2967-2994, December.
    7. Wentao Wang & Jiaxuan Liang & Rong Liu & Yunquan Song & Min Zhang, 2022. "A Robust Variable Selection Method for Sparse Online Regression via the Elastic Net Penalty," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    8. Park, Seyoung & Kim, Hyunjin & Lee, Eun Ryung, 2023. "Regional quantile regression for multiple responses," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    9. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.
    10. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    11. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    12. Karsten Schweikert, 2022. "Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors," Papers 2201.05430, arXiv.org, revised Sep 2024.
    13. 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.
    14. Chen, Yang & Luo, Ziyan & Kong, Lingchen, 2021. "ℓ2,0-norm based selection and estimation for multivariate generalized linear models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    15. Shichen Liao & Congying Han & Tiande Guo & Bonan Li, 2024. "Subspace Newton method for sparse group $$\ell _0$$ ℓ 0 optimization problem," Journal of Global Optimization, Springer, vol. 90(1), pages 93-125, September.
    16. Haruki Kono & Tatsuya Kubokawa, 2023. "Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1022-1047, September.
    17. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    18. Abolghasemi, Mahdi & Tarr, Garth & Bergmeir, Christoph, 2024. "Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 597-615.

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