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A Bayesian approach to analyse overdispersed longitudinal count data

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journal contribution
posted on 2016-01-05, 14:53 authored by Fernanda B. Rizzato, Roseli A. Leandro, Clarice G.B. Demétrio, Geert Molenberghs

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].

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