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dc.contributor.author임용빈-
dc.date.accessioned2016-08-27T02:08:30Z-
dc.date.available2016-08-27T02:08:30Z-
dc.date.issued2001-
dc.identifier.issn0266-4763-
dc.identifier.otherOAK-584-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/215392-
dc.description.abstractThis paper provides a practical simulation-based Bayesian analysis of parameter-driven models for time series Poisson data with the AR(1) latent process. The posterior distribution is simulated by a Gibbs sampling algorithm. Full conditional posterior distributions of unknown variables in the model are given in convenient forms for the Gibbs sampling algorithm. The case with missing observations is also discussed. The methods are applied to real polio data from 1970 to 1983.-
dc.languageEnglish-
dc.publisherCARFAX PUBLISHING-
dc.titleBayesian analysis of time series Poisson data-
dc.typeArticle-
dc.relation.issue2-
dc.relation.volume28-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage259-
dc.relation.lastpage271-
dc.relation.journaltitleJOURNAL OF APPLIED STATISTICS-
dc.identifier.wosidWOS:000165844600009-
dc.author.googleOh, MS-
dc.author.googleLim, YB-
dc.contributor.scopusid임용빈(24370019400)-
dc.date.modifydate20210729145755-
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자연과학대학 > 통계학전공 > Journal papers
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