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dc.contributor.authorGschlößl, Susanneen_US
dc.contributor.authorCzado, Claudiaen_US
dc.date.accessioned2006-04-25en_US
dc.date.accessioned2010-05-14T10:10:32Z-
dc.date.available2010-05-14T10:10:32Z-
dc.date.issued2006en_US
dc.identifier.piurn:nbn:de:bvb:19-epub-1843-7-
dc.identifier.urihttp://hdl.handle.net/10419/31105-
dc.description.abstractIn this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson distribution are addressed where overdispersion is modelled by an additional model parameter. Further, zero-inflated models in which overdispersion is assumed to be caused by an excessive number of zeros are discussed. On the other hand, extra spatial variability in the data is taken into account by adding spatial random effects to the models. This approach allows for an underlying spatial dependency structure which is modelled using a conditional autoregressive prior based on Pettitt et al. (2002). In an application the presented models are used to analyse the number of invasive meningococcal disease cases in Germany in the year 2004. Models are compared according to the deviance information criterion (DIC) suggested by Spiegelhalter et al. (2002) and using proper scoring rules, see for example Gneiting and Raftery (2004). We observe a rather high degree of overdispersion in the data which is captured best by the GP model when spatial effects are neglected. While the addition of spatial effects to the models allowing for overdispersion gives no or only little improvement, a spatial Poisson model is to be preferred over all other models according to the considered criteria.en_US
dc.language.isoengen_US
dc.publisherTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen Münchenen_US
dc.relation.ispartofseriesDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 475en_US
dc.subject.ddc310en_US
dc.subject.stwZähldatenmodellen_US
dc.subject.stwRegressionen_US
dc.subject.stwBayes-Statistiken_US
dc.subject.stwStatistische Verteilungen_US
dc.subject.stwRäumliche Verteilungen_US
dc.subject.stwInfektionskrankheiten_US
dc.subject.stwDeutschlanden_US
dc.titleModelling count data with overdispersion and spatial effectsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn510830854en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:Discussion papers, SFB 386, LMU München

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