Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study

Abstract

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis via simulation studies. Real data on contact wrestling are used for illustration.

Keywords

Estimation methods; overdispersion; Poisson generalized linear mixed models; simulation study; statistical principles; sport injuries

Published in

SORT
2015, Volume: 39, number: 2, pages: 281-307 Publisher: INST ESTADISTICA CATALUNYA-IDESCAT

UKÄ Subject classification

Genetics and Breeding


Permanent link to this page (URI)

https://res.slu.se/id/publ/71184