ABSTRACT
Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this talk, we study an approach to the nonparametric estimation of mixed-effect models. We consider models with parametric random effects and flexible fixed effects, and employ the penalized least squares (Henderson's joint likelihood) method to estimate the models. The issue to be addressed is the selection of smoothing parameters through Mallows' $C_L$ and the generalized cross-validation method, which is shown to yield optimal smoothing for both real and latent random effects.  Simulation studies are conducted to investigate the empirical performance of Mallows' $C_L$ and generalized cross-validation in the context. Real data example is presented to demonstrate the applications of the methodology. The optimal smoothing in generalized nonparametric mixed-effect models is also discussed.

The talk is based on joint work with Chong Gu.

Refreshments will be served at 3:45 p.m. in Room 008 of the Statistics Building