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dc.contributor.authorSarabia Alegría, José María 
dc.contributor.authorShahtahmassebi, Golnaz
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2019-05-30T07:09:01Z
dc.date.available2019-05-30T07:09:01Z
dc.date.issued2015
dc.identifier.issn0361-0918
dc.identifier.issn1532-4141
dc.identifier.otherECO2013- 48326-C2-2-Pes_ES
dc.identifier.urihttp://hdl.handle.net/10902/16289
dc.description.abstractABSTRACT: In this paper, a class of conjugate prior for estimating incomplete count data based on a broad class of conjugate prior distributions is presented. The new class of prior distributions arises from a conditional perspective, making use of the conditional specification methodology and can be considered as the generalisation of the form of prior distributions that have been used previously in the estimation of incomplete count data well. Finally, some examples of simulated and real data are given.es_ES
dc.description.sponsorshipFirst author thanks to the Ministerio de Economía y Competitividad (ECO2013-48326-C2-2-P and Proyecto Puente) for partial support of this work.es_ES
dc.format.extent25 p.es_ES
dc.language.isoenges_ES
dc.publisherTaylor and Francis Inc.es_ES
dc.rights© Taylor & Francis. This an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Simulation and Computation on 2015, available online: ttp://www.tandfonline.com/10.1080/03610918.2015.1091076es_ES
dc.sourceCommunications in Statistics - Simulation and Computation Volume 46, 2017 - Issue 5 , Pages 3419-3435es_ES
dc.subject.otherConditional speci cationes_ES
dc.subject.otherBayesian analysises_ES
dc.subject.otherTruncated gamma distributiones_ES
dc.subject.otherConfluent hypergeometric distributiones_ES
dc.titleBayesian Estimation of Incomplete Data Using Conditionally Specified Priorses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1080/03610918.2015.1091076
dc.type.versionacceptedVersiones_ES


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