Simultaneous variable selection for heteroscedastic regression models

被引：0

作者：

ZhongZhan Zhang

DaRong Wang

机构：

[1] Beijing University of Technology,College of Applied Sciences

[2] Beijing University of Technology,The Pilot College

来源：

Science China Mathematics | 2011年 / 54卷

关键词：

variable selection; heteroscedastic regression models; adjusted profile log-likelihood; AIC; BIC; 62F07; 62J05;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, we propose a new criterion, named PICa, to simultaneously select explanatory variables in the mean model and variance model in heteroscedastic linear models based on the model structure. We show that the new criterion can select the true mean model and a correct variance model with probability tending to 1 under mild conditions. Simulation studies and a real example are presented to evaluate the new criterion, and it turns out that the proposed approach performs well.

引用

页码：515 / 530

页数：15

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