Variable selection for the single-index model

• 成果类型: Article
• 署名作者: Kong, Efang; Xia, Yingcun
• 署名单位: National University of Singapore
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm008
• 发表日期: 2007
• 页码: 217229
• 关键词: nonlinear time-series; semiparametric estimation

摘要

We consider variable selection in the single-index model. We prove that the popular leave-m-out crossvalidation method has different behaviour in the single-index model from that in linear regression models or nonparametric regression models. A new consistent variable selection method, called separated crossvalidation, is proposed. Further analysis suggests that the method has better finite-sample performance and is computationally easier than leave-m-out crossvalidation. Separated crossvalidation, applied to the Swiss banknotes data and the ozone concentration data, leads to single-index models with selected variables that have better prediction capability than models based on all the covariates.