Simultaneous variable selection for heteroscedastic regression models

被引：0

作者：

Zhang, Zhongzhan ^{[1
]}

Wang, Darong ^{[1
]}

机构：

[1] Beijing Univ Technol, Dept Appl Math, Beijing 100124, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON FINANCIAL ENGINEERING AND RISK MANAGEMENT 2008 | 2008年

关键词：

model selection; heteroscedastic regression models; adjusted profile log-likelihood; Kullback-Leibler information; AIC; BIC;

D O I：

暂无

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

The simultaneous variable selection for mean model and variance model in heteroscedastic linear models is discussed in this paper. We propose a criterion named PIC based on the adjusted profile log-likelihood function, which can be employed to jointly select regression variables in the mean model and variance model. The proposed criterion is compared with the naive AIC and BIC through a Monte Carlo simulation, and it is shown that PIC outperforms AIC, and is comparable with BIC. In addition, when the sample size is not large, it performs the best.

引用

页码：141 / 143

页数：3

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