Variable Selection for Generalized Varying Coefficient Partially Linear Models with Diverging Number of Parameters  

作　　者：Zheng-yan Lin Yu-ze Yuan 

机构地区：[1]Department of Mathematics, Zhejiang University, Hangzhou 310027, China

出　　处：《Acta Mathematicae Applicatae Sinica》2012年第2期237-246,共10页应用数学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 10871177);Specialized Research Fund for the Doctoral Program of Higher Education (No. 20090101110020)

摘　　要：Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components： model selection for non-parametric components and selection of significant variables for the parametric portion. In this paper, we consider a variable selection procedure by combining basis function approximation with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of tuning parameters, we establish the consistency and sparseness of this procedure.Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components： model selection for non-parametric components and selection of significant variables for the parametric portion. In this paper, we consider a variable selection procedure by combining basis function approximation with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of tuning parameters, we establish the consistency and sparseness of this procedure.

关 键 词：generalized linear model varying coefficient high dimensionality SCAD basis function. 

分 类 号：O175.29[理学—数学] V247[理学—基础数学]