Variable selection for the single index model

Kong, E. (2007) Variable selection for the single index model. Biometrika, 94 (1). pp. 217-229. ISSN 0006-3444. (Full text available)

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Official URL
http://dx.doi.org/10.1093/biomet/asm008

Abstract

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
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Efang Kong
Date Deposited: 29 Mar 2010 11:37
Last Modified: 31 Jul 2012 08:00
Resource URI: http://kar.kent.ac.uk/id/eprint/23951 (The current URI for this page, for reference purposes)
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