A Bayesian approach to analyse overdispersed longitudinal count data

Fernanda B. Rizzato, Roseli A. Leandro, Clarice G.B. Demétrio and Geert Molenberghs

Journal of Applied Statistics, 2016, vol. 43, issue 11, 2085-2109

Abstract: In this paper, we consider a model for repeated count data, with within-subject correlation and/or overdispersion. It extends both the generalized linear mixed model and the negative-binomial model. This model, proposed in a likelihood context [17,18] is placed in a Bayesian inferential framework. An important contribution takes the form of Bayesian model assessment based on pivotal quantities, rather than the often less adequate DIC. By means of a real biological data set, we also discuss some Bayesian model selection aspects, using a pivotal quantity proposed by Johnson [12].

Date: 2016
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DOI: 10.1080/02664763.2015.1126812

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