Smoothing in Nonparametric Mixed-Effect Models
Department of Statistics
Thursday, 10 April 2003
E105 Engineering Building
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