Consistent Model Selection and Data-driven Smooth Tests for Longitudinal Data in the Estimating Equations Approach

Lan Wang
School of Statistics
University of Minnesota

Wednesday, October 18th 2006
3:30pm
Moos 2-690
Minneapolis Campus

Abstract:
An important problem facing marginal regression analysis of longitudinal data, as in the method of generalized estimating equations, is how to choose a marginal regression model from a number of candidate models. Although several methods have been suggested in the literature for practical use, theoretical investigation of the large sample theory is still lacking. We propose a new BIC-type model selection criterion in this paper, and prove that with probability approaching one it selects the most parsimonious correct model. The model selection criterion uses the quadratic inference function proposed by Qu, Lindsay and Li (2000) and does not need to specify the full likelihood or quasilikelihood. This model selection procedure also motivates a data-driven Neyman-type smooth test for checking the goodness-of-fit of a conjectured model. Compared to the classical tests which require the specification of an alternative, such as the GEE Z-test, the new test selects a data-driven alternative based on model selection and leads to increased power performance in general. The finite sample performance of the new model selection and model checking procedures is demonstrated through Monte Carlo studies and analysis of a clinical trial data set. (Joint work with Annie Qu)

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html