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.
Volume (Year): 89 (2007)
Issue (Month): 4 (November)
|Contact details of provider:|| Web page: http://mitpress.mit.edu/journals/|
|Order Information:||Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535|
When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:89:y:2007:i:4:p:784-789. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Pollock-Nelson)
If references are entirely missing, you can add them using this form.