Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asm008
Title: Variable selection for the single-index model
Authors: Kong, E.
Xia, Y. 
Keywords: Consistency
Crossvalidation
Nonparametric smoothing
Semiparametric model
Variable selection
Issue Date: Mar-2007
Citation: Kong, E., Xia, Y. (2007-03). Variable selection for the single-index model. Biometrika 94 (1) : 217-229. ScholarBank@NUS Repository. https://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. © 2007 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105459
ISSN: 00063444
DOI: 10.1093/biomet/asm008
Appears in Collections:Staff Publications

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