Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors
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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.
author list (cited authors)
Hall, P., Li, Q. i., & Racine, J. S.