Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16620
Title: Modeling multivariate, overdispersed binomial data with additive and multiplicative random effects
Authors: DEL FAVA, Emanuele 
SHKEDY, Ziv 
AREGAY, Mehreteab 
MOLENBERGHS, Geert 
Issue Date: 2014
Source: Statistical modelling, 14 (2), p. 99-133
Abstract: Often, when modeling longitudinal binomial data, one needs to take into consideration both clustering and overdispersion. When the primary interest is in accommodating both phenomena, we can use separate sets of random effects that capture the within-cluster association and the extra variability due to overdispersion. In this paper, we propose a series of hierarchical Bayesian generalized linear mixed models that deal simultaneously with both phenomena. The proposed models are applied to a sample of multivariate data on hepatitis C virus (HCV) and human immunodeficiency virus (HIV) infection prevalence in injecting drug users in Italy from 1998 to 2007.
Notes: Del Fava, E (reprint author),Bocconi Univ, Carlo F Dondena Ctr Res Social Dynam, Via Gugliemo Rontgen 1, I-20136 Milan, Italy, emanuele.delfava@unibocconi.it
Keywords: binomial data; clustering; generalized linear mixed models; MCMC; overdispersion.
Document URI: http://hdl.handle.net/1942/16620
ISSN: 1471-082X
e-ISSN: 1477-0342
DOI: 10.1177/1471082X13503450
ISI #: 000334290800001
Category: A1
Type: Journal Contribution
Validations: ecoom 2015
Appears in Collections:Research publications

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