The use of random-effect models for high-dimensional variable selection problems

被引：10
|
作者：
Kwon, Sunghoon [1 ]
Oh, Seungyoung [2 ]
Lee, Youngjo [2 ]
机构：
[1] Konkuk Univ, Dept Appl Stat, Seoul, South Korea
[2] Seoul Natl Univ, Dept Stat, Seoul, South Korea
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2016年 / 103卷
基金：
新加坡国家研究基金会;
关键词：
Generalized linear model; Hierarchical likelihood; High-dimension; Random effect; Unbounded penalty; Variable selection; NONCONCAVE PENALIZED LIKELIHOOD; DIVERGING NUMBER; LINEAR-MODELS; REGRESSION; REGULARIZATION; CLASSIFICATION; MICROARRAYS; SHRINKAGE; LASSO;
D O I：
10.1016/j.csda.2016.05.016
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We study the use of random-effect models for variable selection in high-dimensional generalized linear models where the number of covariates exceeds the sample size. Certain distributional assumptions on the random effects produce a penalty that is non-convex and unbounded at the origin. We introduce a unified algorithm that can be applied to various statistical models including generalized linear models. Simulation studies and data analysis are provided. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：401 / 412
页数：12
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