Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure

作     者：Wang, Xiuli Wang, Mingqiu 

作者机构：Qufu Normal Univ Sch Stat Qufu Peoples R China 

出 版 物：《JOURNAL OF APPLIED STATISTICS》 (应用统计学杂志)

年 卷 期：2016年第43卷第5期

页      面：796-809页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：National Natural Science Foundation of China China Postdoctoral Science Foundation [2014M561892] 

主　　题：collinearity generalized linear models nonconvex penalty oracle ridge estimator variable selection 

摘      要：High-dimensional data arise frequently in modern applications such as biology, chemometrics, economics, neuroscience and other scientific fields. The common features of high-dimensional data are that many of predictors may not be significant, and there exists high correlation among predictors. Generalized linear models, as the generalization of linear models, also suffer from the collinearity problem. In this paper, combining the nonconvex penalty and ridge regression, we propose the weighted elastic-net to deal with the variable selection of generalized linear models on high dimension and give the theoretical properties of the proposed method with a diverging number of parameters. The finite sample behavior of the proposed method is illustrated with simulation studies and a real data example.