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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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