dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade de São Paulo (USP)
dc.date.accessioned2018-12-11T16:38:16Z
dc.date.available2018-12-11T16:38:16Z
dc.date.created2018-12-11T16:38:16Z
dc.date.issued2015-12-01
dc.identifierInternational Journal of Industrial Engineering Computations, v. 6, n. 1, p. 511-524, 2015.
dc.identifier1923-2934
dc.identifier1923-2926
dc.identifierhttp://hdl.handle.net/11449/167771
dc.identifier10.5267/j.ijiec.2014.8.002
dc.identifier2-s2.0-84923612961
dc.description.abstractThe generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.
dc.languageeng
dc.relationInternational Journal of Industrial Engineering Computations
dc.relation0,537
dc.relation0,537
dc.rightsAcesso restrito
dc.sourceScopus
dc.subjectBayesian analysis
dc.subjectDistribution
dc.subjectGeneralized exponential
dc.subjectMCMC methods
dc.subjectNon-informative priors
dc.titleGeneralized exponential distribution: A Bayesian approach using MCMC methods
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución