This paper studies the asymptotic and nite-sample performance ofpenalized regression methods when different selectors of theregularization parameter are used under the assumption that the truemodel is, or is not, included among the candidate model. In the lattersetting, we relax assumptions in...

This paper studies the asymptotic and nite-sample performance of penalized regression methods when different selectors of the regularization parameter are used under the assumption that the true model is, or is not, included among the candidate model. In the latter setting, we relax assumptions...

There are many different missing data methods used by classification tree algorithms, but few studies have been done comparing their appropriateness and performance. This paper provides both analytic and Monte Carlo evidence regarding the effectiveness of six popular missing data methods for...

Nonparametric regression techniques provide an e ective way of identifying and examiningstructure in regression data The standard approaches to nonparametric regression suchas local polynomial and smoothing spline estimators are sensitive to unusual observations and alternatives designed to be...