dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMoala, Fernando Antonio
dc.creatorRamos, Pedro Luiz
dc.creatorAchcar, Jorge Alberto
dc.date2014-12-03T13:09:11Z
dc.date2016-10-25T20:10:18Z
dc.date2014-12-03T13:09:11Z
dc.date2016-10-25T20:10:18Z
dc.date2013-12-01
dc.date.accessioned2017-04-06T06:18:46Z
dc.date.available2017-04-06T06:18:46Z
dc.identifierRevista Colombiana de Estadistica. Bogota Dc: Univ Nac Colombia, Dept Estadistica, v. 36, n. 2, p. 321-338, 2013.
dc.identifier0120-1751
dc.identifierhttp://hdl.handle.net/11449/112051
dc.identifierhttp://acervodigital.unesp.br/handle/11449/112051
dc.identifierWOS:000331380600009
dc.identifierWOS000331380600009.pdf
dc.identifierhttp://revistas.unal.edu.co/index.php/estad/article/view/44351
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/922819
dc.descriptionIn this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors.
dc.languageeng
dc.publisherUniv Nac Colombia, Dept Estadistica
dc.relationRevista Colombiana De Estadistica
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGamma distribution
dc.subjectnoninformative prior
dc.subjectcopula
dc.subjectconjugate
dc.subjectJeffreys prior
dc.subjectreference
dc.subjectMDIP
dc.subjectorthogonal
dc.subjectMCMC
dc.titleBayesian inference for two-parameter gamma distribution assuming different noninformative priors
dc.typeOtro


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