Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors
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. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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Volume (Year): 89 (2007)
Issue (Month): 4 (November)
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