Bayesian Estimation And Case Influence Diagnostics For The Zero-inflated Negative Binomial Regression Model

dc.creatorGaray A.M.
dc.creatorLachos V.H.
dc.creatorBolfarine H.
dc.date2015
dc.date2015-06-25T12:51:04Z
dc.date2015-11-26T15:27:44Z
dc.date2015-06-25T12:51:04Z
dc.date2015-11-26T15:27:44Z
dc.date.accessioned2018-03-28T22:36:24Z
dc.date.available2018-03-28T22:36:24Z
dc.identifier
dc.identifierJournal Of Applied Statistics. Taylor And Francis Ltd., v. 42, n. 6, p. 1148 - 1165, 2015.
dc.identifier2664763
dc.identifier10.1080/02664763.2014.995610
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84924917320&partnerID=40&md5=b900327fe15315b294e7c06959d96595
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/85205
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85205
dc.identifier2-s2.0-84924917320
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1261417
dc.descriptionIn recent years, there has been considerable interest in regression models based on zero-inflated distributions. These models are commonly encountered in many disciplines, such as medicine, public health, and environmental sciences, among others. The zero-inflated Poisson (ZIP) model has been typically considered for these types of problems. However, the ZIP model can fail if the non-zero counts are overdispersed in relation to the Poisson distribution, hence the zero-inflated negative binomial (ZINB) model may be more appropriate. In this paper, we present a Bayesian approach for fitting the ZINB regression model. This model considers that an observed zero may come from a point mass distribution at zero or from the negative binomial model. The likelihood function is utilized to compute not only some Bayesian model selection measures, but also to develop Bayesian case-deletion influence diagnostics based on q-divergence measures. The approach can be easily implemented using standard Bayesian software, such as WinBUGS. The performance of the proposed method is evaluated with a simulation study. Further, a real data set is analyzed, where we show that ZINB regression models seems to fit the data better than the Poisson counterpart.
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dc.languageen
dc.publisherTaylor and Francis Ltd.
dc.relationJournal of Applied Statistics
dc.rightsfechado
dc.sourceScopus
dc.titleBayesian Estimation And Case Influence Diagnostics For The Zero-inflated Negative Binomial Regression Model
dc.typeArtículos de revistas


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