  Mixed-effect models are widely used for the analysis of correlated data such
as longitudinal data and repeated measures. In this article, 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 method to estimate the models. The issue to be
addressed is the selection of smoothing parameters through 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 generalized cross-validation in the context.
Real-data examples are presented to demonstrate the applications of the
methodology.
