The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade de São Paulo (USP)
dc.date.accessioned2015-03-18T15:54:04Z
dc.date.available2015-03-18T15:54:04Z
dc.date.created2015-03-18T15:54:04Z
dc.date.issued2014-11-01
dc.identifierStatistical Methodology. Amsterdam: Elsevier Science Bv, v. 21, p. 23-34, 2014.
dc.identifier1572-3127
dc.identifierhttp://hdl.handle.net/11449/116751
dc.identifier10.1016/j.stamet.2013.11.003
dc.identifierWOS:000340336800002
dc.identifier5267593860042306
dc.description.abstractIn this paper we propose the zero-inflated COM-Poisson distribution. We develop a Bayesian analysis for our model via on Markov chain Monte Carlo methods. We discuss regression modeling and model selection, as well as, develop case deletion influence diagnostics for the joint posterior distribution based on the psi-divergence, which has several divergence measures as particular cases, such as the Kullback-Leibler (K-L), J-distance, L-1 norm and chi(2)-square divergence measures. The performance of our approach is illustrated in an artificial dataset as well as in a real dataset on an apple cultivar experiment. (C) 2014 Elsevier B.V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationStatistical Methodology
dc.relation0,378
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectBayesian inference
dc.subjectCOM-Poisson distribution
dc.subjectKullback-Leibler distance
dc.subjectZero-inflated models
dc.titleThe zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic
dc.typeArtículos de revistas


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