Trimmed LASSO regression estimator for binary response data

被引：3
作者：
Sun, Hongwei [1 ,2 ]
Cui, Yuehua [3 ]
Gao, Qian [1 ]
Wang, Tong [1 ]
机构：
[1] Shanxi Med Univ, Sch Publ Hlth, Dept Hlth Stat, Taiyuan 030001, Shanxi, Peoples R China
[2] Binzhou Med Univ, Sch Publ Hlth & Management, Dept Hlth Stat, Yantai 264003, Shandong, Peoples R China
[3] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2020年 / 159卷
基金：
中国国家自然科学基金;
关键词：
Penalized logistic regression; Maximum trimmed likelihood; LASSO; Breakdown point; Variable selection; LOGISTIC-REGRESSION; SELECTION;
D O I：
10.1016/j.spl.2019.108679
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
A robust LASSO-type penalized logistic regression based on maximum trimmed likelihood is proposed. The robustness property of the proposed method is stated and proved. A comparison of the performances of the proposed method with regular LASSO was conducted via simulations. (C) 2019 Elsevier B.V. All rights reserved.
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页数：7
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