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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 | Source: | 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 | Keywords: | Bayesian analysis; Bayesian model assessment; count data; generalized linear mixed model; over dispersion | Document URI: | http://hdl.handle.net/1942/20868 | ISSN: | 0266-4763 | e-ISSN: | 1360-0532 | DOI: | 10.1080/02664763.2015.1126812 | ISI #: | 000382570500009 | Rights: | © 2015 Taylor & Francis | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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477.pdf Restricted Access | Published version | 2.65 MB | Adobe PDF | View/Open Request a copy |
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