Naisyin Wang, Texas A&M University 

Vendredi 29 aout 2008 à 11h15, salle 5220:

The analysis of hierarchical biomedical data sometimes requires more modeling flexibility than that can be provided by standard parametric approaches. It is commonly believed that the effect of ignoring covariance structure is mainly on the lost of efficiency. In this talk, I will discuss some recently developed nonparametric or semiparametric models for longitudinal observations. I will use numerical outcomes and examples to illustrate some potential concerns when one ignores the correlations in longitudinal measurements. The less known fact is the serious biases that could be induced by ignoring correlations in the longitudinal covariate observations. The modeling consideration of the use of functional principle component analysis in a recently developed nonparametric latent-feature regression model will also be discussed.