Student Seminar Series - November 10, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
Combining
Linear Models in Longitudinal Data Analysis
Song Liu
Thursday, November 10, 2005
9:30 AM, 13
Rapson Hall (Architecture)
Minneapolis, East Bank Campus
Refreshments at 9:15 AM
300 Ford Hall
Abstract
Longitudinal
data are the samples from a number of subjects, each measured
repeatedly over a time period, very common in biomedical studies.
Varieties of methods analyzing longitudinal data have been proposed.
However, there exist gaps in model selection techniques for
longitudinal data analysis, even for linear (mixed) models. Among the
several existing selection techniques information criteria, such as AIC
and BIC, have been widely applied. To deal with uncertainty in model
selection, model combining methods have been developed in recent years.
Its advantages over model selection have been demonstrated both
empirically and theoretically. In this paper, we illustrate the
uncertainty problem with model selection for longitudinal data by
examples, and we propose a model combining method for estimating
marginal means. Its theoretical properties are examined and numerical
examples are presented. The results support model combining methods
when model selection uncertainty is non-negligible.