Objective priors for estimation of extended exponential geometric distribution

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T19:01:05Z
dc.date.accessioned2022-12-20T00:58:29Z
dc.date.available2022-04-28T19:01:05Z
dc.date.available2022-12-20T00:58:29Z
dc.date.created2022-04-28T19:01:05Z
dc.date.issued2014-01-01
dc.identifierJournal of Modern Applied Statistical Methods, v. 13, n. 2, p. 226-243, 2014.
dc.identifier1538-9472
dc.identifierhttp://hdl.handle.net/11449/220372
dc.identifier10.22237/jmasm/1414815060
dc.identifier2-s2.0-84930344712
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5400501
dc.description.abstractA Bayesian analysis was developed with different noninformative prior distributions such as Jeffreys, Maximal Data Information, and Reference. The aim was to investigate the effects of each prior distribution on the posterior estimates of the parameters of the extended exponential geometric distribution, based on simulated data and a real application.
dc.languageeng
dc.relationJournal of Modern Applied Statistical Methods
dc.sourceScopus
dc.subjectBayesian
dc.subjectExtended exponential geometric distribution
dc.subjectJeffreys
dc.subjectMDIP
dc.subjectNoninformative
dc.subjectPrior
dc.subjectReference
dc.titleObjective priors for estimation of extended exponential geometric distribution
dc.typeArtículos de revistas


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