Simultaneous variable selection for heteroscedastic regression models

摘要：

The simultaneous variable selection for mean model and variance model in heteroscedastie 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 A/C, and is comparable with BIC. In addition, when the sample size is not large, it performs the best,

关键词： model selection heteroscedastie regression models adjusted profile log-likelihood Kullback-Leibler information AIC BIC

作者: Zhongzhan ZHANG Darong WANG

作者单位: Department of Applied Mathematics,Beijing University of Technology,Beijing,100124,P.R.China

会议类型: 国际会议

会议名称: International Symposium on Financial Engineering and Risk Management(2008年金融工程与风险管理)

会议地点: 上海

会议语种:英文

页码: 141-143

在线出版日期: 2008-06-01（万方平台首次上网日期，不代表论文的发表时间）