Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14593
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dc.contributor.authorAREGAY, Mehreteab-
dc.contributor.authorSHKEDY, Ziv-
dc.contributor.authorMOLENBERGHS, Geert-
dc.date.accessioned2013-02-21T08:45:31Z-
dc.date.available2013-02-21T08:45:31Z-
dc.date.issued2013-
dc.identifier.citationCOMPUTATIONAL STATISTICS & DATA ANALYSIS, 57 (1), p. 233-245-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/1942/14593-
dc.description.abstractIn sets of count data, the sample variance is often considerably larger or smaller than the sample mean, known as a problem of over- or underdispersion. The focus is on hierarchical Bayesian modeling of such longitudinal count data. Two different models are considered. The first one assumes a Poisson distribution for the count data and includes a subject-specific intercept, which is assumed to follow a normal distribution, to account for subject heterogeneity. However, such a model does not fully address the potential problem of extra-Poisson dispersion. The second model, therefore, includes also random subject and time dependent parameters, assumed to be gamma distributed for reasons of conjugacy. To compare the performance of the two models, a simulation study is conducted in which the mean squared error, relative bias, and variance of the posterior means are compared. (C) 2012 Elsevier B.V. All rights reserved.-
dc.description.sponsorshipThe authors gratefully acknowledge the support from IAP research Network P6/03 of the Belgian Government (Belgian Science Policy). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI.-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.rights© 2012 Elsevier B.V. All rights reserved-
dc.subject.otherDeviance information criteria; Hierarchical Poisson-Normal model (HPN); Hierarchical Poisson-Normal overdispersed model (HPNOD); Overdispersion-
dc.subject.otherComputer Science, Interdisciplinary Applications; Statistics & Probability; deviance information criteria; Hierarchical Poisson-Normal model (HPN); Hierarchical Poisson-Normal overdispersed model (HPNOD); overdispersion-
dc.titleA hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study-
dc.typeJournal Contribution-
dc.identifier.epage245-
dc.identifier.issue1-
dc.identifier.spage233-
dc.identifier.volume57-
local.format.pages13-
local.bibliographicCitation.jcatA1-
dc.description.notes[Shkedy, Ziv; Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Aregay, Mehreteab; Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Louvain, Belgium. geert.molenberghs@uhasselt.be-
local.publisher.placeAMSTERDAM-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.type.programmeVSC-
dc.identifier.doi10.1016/j.csda.2012.06.020-
dc.identifier.isi000310403700017-
item.validationecoom 2013-
item.fulltextWith Fulltext-
item.contributorAREGAY, Mehreteab-
item.contributorMOLENBERGHS, Geert-
item.contributorSHKEDY, Ziv-
item.fullcitationAREGAY, Mehreteab; SHKEDY, Ziv & MOLENBERGHS, Geert (2013) A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 57 (1), p. 233-245.-
item.accessRightsOpen Access-
crisitem.journal.issn0167-9473-
crisitem.journal.eissn1872-7352-
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