A Bayesian approach to analyse overdispersed longitudinal count data. Issue 11 (17th August 2016) - Rizzato, Fernanda B.; Leandro, Roseli A.; Demétrio, Clarice G.B.; Molenberghs, Geert
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 ].