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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20868

Title: A Bayesian approach to analyse overdispersed longitudinal count data
Authors: Rizzato, F.B.
Leandro, R.A.
Demétrio, C.G.B.
Molenberghs, Geert
Issue Date: 2016
Citation: Journal of applied statistics, 43(11), p. 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].
Notes: Rizzato, FB (reprint author), Univ Fed Parana, Stat, Curitiba, Parana, Brazil. fernandab@ufpr.br
URI: http://hdl.handle.net/1942/20868
DOI: 10.1080/02664763.2015.1126812
ISI #: 000382570500009
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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