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论文类型：期刊论文

发表时间：2013-11-02

发表刊物：COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

收录刊物：EI、SCIE、Scopus

卷号：42

期号：21

页面范围：3873-3888

ISSN号：0361-0926

关键字：Canonical link function; Model selection; Multivariate generalized linear model; Smoothly clipped absolute deviation; Tuning parameter; 62J07; 62J12

摘要：Variable selection is fundamental to high-dimensional multivariate generalized linear models. The smoothly clipped absolute deviation (SCAD) method can solve the problem of variable selection and estimation. The choice of the tuning parameter in the SCAD method is critical, which controls the complexity of the selected model. This article proposes a criterion to select the tuning parameter for the SCAD method in multivariate generalized linear models, which is shown to be able to identify the true model consistently. Simulation studies are conducted to support theoretical findings, and two real data analysis are given to illustrate the proposed method.