Robust variable selection based on the random quantile LASSO-Reference-Cited by-同舟云学术

Robust variable selection based on the random quantile LASSO

Published:2019-07-22 Issue:1 Volume:51 Page:29-39
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Wang Yan¹,Jiang Yunlu¹,Zhang Jiantao¹,Chen Zhongran²,Xie Baojian³,Zhao Chengxiang⁴

Affiliation:

1. Department of Statistics College of Economics, Jinan University, Guangzhou, Guangdong, P. R. China

2. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, P. R. China

3. College of Economics, Jinan University, Guangzhou, Guangdong, P. R. China

4. Department of Human Resources, Jinan University, Guangzhou, Guangdong, P. R. China

Funder

Natural Science Foundation of Guangdong

National Statistical Scientific Research

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2019.1643886

Reference36 articles.

1. Sparse least trimmed squares regression for analyzing high-dimensional large data sets

2. Bar, H., J. Booth, and M. T. Wells. 2018. A scalable empirical Bayes approach to variable selection in generalized linear models. arXiv preprint, arXiv:1803.09735.

3. ℓ1-penalized quantile regression in high-dimensional sparse models

4. Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems

5. Penalized composite quasi-likelihood for ultrahigh dimensional variable selection

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