presents

Dr. Suojin Wang

of

Texas A & M

Generalized empirical likelihood methods for analyzing longitudinal data
Abstract:

Efficient estimation of parameters is a major objective in analyzing
longitudinal data. In this work, we propose two generalized empirical
likelihood based methods that take into consideration within-subject
correlations. A nonparametric version of the Wilks theorem for the
limiting distributions of the empirical likelihood ratios is derived.
It is shown that one of the proposed methods is locally efficient
among a class of within-subject variance-covariance matrices. A
simulation study is carried out to investigate the finite sample
properties of the proposed methods and compare them with the block
empirical likelihood method by You, et al. (2006) and the normal
approximation with a correctly estimated variance-covariance. The
results suggest that the proposed methods are generally more efficient
than existing methods which ignore the correlation structure, and
better in coverage compared to the normal approximation with correctly
specified within-subject correlation for small to moderate sample
sizes. An application of the proposed procedures to the Framingham
Heart Study is illustrated.

3:00pm - 4:00pm
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