Nonconcave penalized M-estimation for the least absolute relative errors model

被引：3
|
作者：
Fan, Ruiya [1 ,2 ]
Zhang, Shuguang [1 ]
Wu, Yaohua [1 ]
机构：
[1] Univ Sci & Technol China, Dept Stat & Finance, Hefei, Anhui, Peoples R China
[2] Southwest Univ Sci & Technol, Dept Math, Mianyang, Sichuan, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2023年 / 52卷 / 04期
关键词：
Multiplicative regression model; M-estimation; LARE; variable selection; VARIABLE SELECTION; DIVERGING NUMBER; LIKELIHOOD; REGRESSION; LASSO;
D O I：
10.1080/03610926.2021.1923749
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：
In this paper, we propose a nonconcave penalized M-estimation of the least absolute relative errors (penalized M-LARE) method for a sparse multiplicative regression model, where the dimension of model can increase with the sample size. Under certain appropriate conditions, the consistency and asymptotic normality for the penalized M-LARE estimator are established. Simulations and a real data analysis are in support of our theoretical results and illustrate that the proposed method performs well.
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页码：1118 / 1135
页数：18
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