Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors Academic Article uri icon

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.

altmetric score

  • 3

author list (cited authors)

  • Hall, P., Li, Q. i., & Racine, J. S.

citation count

  • 122

publication date

  • November 2007