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Variable selection for the single index model

Kong, Efang, Xia, Yingcun (2007) Variable selection for the single index model. Biometrika, 94 (1). pp. 217-229. ISSN 0006-3444. (doi:10.1093/biomet/asm008) (KAR id:23951)

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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.

Item Type: Article
DOI/Identification number: 10.1093/biomet/asm008
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Efang Kong
Date Deposited: 29 Mar 2010 11:37 UTC
Last Modified: 28 May 2019 15:18 UTC
Resource URI: (The current URI for this page, for reference purposes)
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