Variable Selection in Longitudinal and Additive Models

Abstract

Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (Marginal Models, Mixed Effects Models, etc.), the important issue of variable selection has been somewhat neglected in this context. In this talk we discuss some recent proposals based on a generalized version of Mallows’s Cp and on cross-validation suitable for use with both parametric and nonparametric models.