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.

引用

页码：1118 / 1135

页数：18

共 46 条

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[46] Local Least Product Relative Error Estimation for Varying Coefficient Multiplicative Regression Model
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