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Variable selection for the single‐index model.
- Source :
-
Biometrika . Mar2007, Vol. 94 Issue 1, p217-229. 13p. - Publication Year :
- 2007
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00063444
- Volume :
- 94
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Biometrika
- Publication Type :
- Academic Journal
- Accession number :
- 24394747
- Full Text :
- https://doi.org/10.1093/biomet/asm008