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Activity Number: 128
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #315170 View Presentation
Title: GMM Versus GQL Logistic Regression Models for Multi-Level Correlated Data
Author(s): Bei Wang* and Jeffrey Wilson
Companies: Arizona State University and W. P. Carey School of Business/Arizona State University
Keywords: GLMMs ; Correlated Data ; Newton-Raphson ; R

The analysis of correlated data denies us the opportunity to present the actual likelihood. Inferences for the regression parameters and the variance of the random effects in the generalized linear mixed models are key in the analysis of the data. In this paper we present a generalized method of moments (GMM) logistic regression model and a generalized quasi-likelihood (GQL) approach as an alternative means of analyzing correlated data. We examine and compare the performance of both approaches in the fit of logistic regression models with multi-levels of random effects. A simulation study is conducted and a numerical example with three levels of nesting is analyzed.

Authors who are presenting talks have a * after their name.

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