JSM 2005 - Toronto

Abstract #302345

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 303
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #302345
Title: GMM and Estimating Functions in Longitudinal Data
Author(s): Annie Qu*+
Companies: Oregon State University
Address: 44 Kidder Hall, Dept. of Statistics, Corvallis, OR, 97331,
Keywords: Generalized method of moments ; quadratic inference function ; non-nested hypothesis ; longitudinal data ; goodness-of-fit
Abstract:

The full parametric likelihood function of the model often is difficult to specify for complex, high-dimensional longitudinal data. A semiparametric model, which is defined by a set of mean zero estimating functions might be useful for estimation. It also occurs that there might be more moment conditions than estimable parameters in longitudinal data settings. The generalized method of moments (GMM) provides an efficient estimation of weights for over-identified estimating functions. In this talk, we compare the GMM approach to the empirical likelihood approach. In addition, we illustrate a goodness-of-fit test for model assumption. In particular, a non-nested hypothesis-testing procedure will be proposed.

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Revised March 2005