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Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304281
Title: Robust Estimation Method for Finite Mixture of Poisson Mixed-Effect Models
Author(s): Kelvin Yau*+ and Liming Xiang and Andy Lee
Companies: City University of Hong Kong and Nanyang Technological University and Curtin University
Address: Dept of Management Sciences, Kowloon, , Hong Kong
Keywords: Finite mixture ; Minimum Hellinger distance ; NPML ; REML ; Robustness

When analyzing clustered count data derived from several latent subpopulations, the finite mixture of Poisson mixed-effect model is an immediate strategy to model the underlying heterogeneity. Within the generalized linear mixed model framework, parameters in such a model are often estimated through the residual maximum likelihood (REML) estimation method. However, the method is vulnerable to outliers. To develop robust estimators, without prescribing a parametric form for the random effects distribution, we consider embedding the non-parametric maximum likelihood (NPML) approach within the minimum Hellinger distance (MHD) estimation method for a finite mixture of Poisson mixed-effect models. The NPML estimation not only avoids the problem of numerical integration in deriving the MHD estimating equations, but also enhances the robustness characteristic because of its resistance to possible misspecification of the parametric distribution for the random effects.

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