Penalized empirical likelihood for high-dimensional generalized linear models

被引：0

作者：

Chen, Xia ^{[1
]}

Mao, Liyue ^{[2
]}

机构：

[1] Shaanxi Normal Univ, Sch Math & Informat Sci, Xian 710119, Peoples R China

[2] Chongqing Coll Elect Engn, Sch Finance & Econ Management, Chongqing 401331, Peoples R China

来源：

STATISTICS AND ITS INTERFACE | 2021年 / 14卷 / 02期

关键词：

Penalized empirical likelihood; High-dimensional data; Variable selection; Generalized linear models; VARIABLE SELECTION; DIVERGING NUMBER; REGRESSION; SHRINKAGE; SCOPE;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We develop penalized empirical likelihood for parameter estimation and variable selection in high-dimensional generalized linear models. By using adaptive lasso penalty function, we show that the proposed estimator has the oracle property. Also, we consider the problem of testing hypothesis, and show that the nonparametric profiled empirical likelihood ratio statistic has asymptotic chi-square distribution. Some simulations and an application are given to illustrate the performance of the proposed method.

引用

页码：83 / 94

页数：12

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