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.

引用

页数：7

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