Title: Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors
Abstract: In this paper we consider a nonparametric regression model that admits a mix of continuous and discrete regressors, some of which may in fact be redundant (that is, irrelevant). We show that, asymptotically, a data-driven least squares cross-validation method can remove irrelevant regressors. Simulations reveal that this “automatic dimensionality reduction” feature is very effective in finite-sample settings.
Publication Year: 2007
Publication Date: 2007-10-11
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 191
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