Statistics and Data Science Seminar
Prof. Peng Wang
Bowling Green State University
Inference Function for Mixed Effects Models and its applications
Abstract: In longitudinal studies, mixed-effects models are important for addressing
subject-specific effects. However, most existing approaches
assume a normal distribution for the random effects, and this could affect the bias and efficiency of the fixed-effects estimator.
Even in cases where the estimation of the fixed effects is robust with a misspecified distribution of the random effects, the
estimation of the random effects could be invalid. We propose a new approach to estimate
fixed and random effects using conditional quadratic inference
functions. The new approach does not require the specification of
likelihood functions or a normality assumption for random effects. It can
also accommodate serial correlation between observations within the same cluster,
in addition to mixed-effects
modeling. Other advantages include not
requiring the estimation of the unknown variance components associated
with the random effects, or the nuisance parameters associated with the working
correlations. We establish asymptotic results for the fixed-effect parameter estimators which do not rely on the consistency of the random-effect estimators.
Some applications of the proposed approach will also be presented.
Wednesday May 1, 2013 at 4:00 PM in SEO 636