Objective and subjective prior distributions for the gompertz distribution

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dc.contributor.authorMoala, Fernando A. [UNESP]
dc.contributor.authorDey, Sanku
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionSt. Anthony’s College
dc.date.accessioned2019-10-06T15:59:41Z
dc.date.available2019-10-06T15:59:41Z
dc.date.issued2018-07-01
dc.description.abstractThis paper takes into account the estimation for the unknown parameters of the Gompertz distribution from the frequentist and Bayesian view points by using both objective and subjective prior distributions. We first derive non-informative priors using formal rules, such as Jefreys prior and maximal data information prior (MDIP), based on Fisher information and entropy, respectively. We also propose a prior distribution that incorporate the expert’s knowledge about the issue under study. In this regard, we assume two independent gamma distributions for the parameters of the Gompertz distribution and it is employed for an elicitation process based on the predictive prior distribution by using Laplace approximation for integrals. We suppose that an expert can summarize his/her knowledge about the reliability of an item through statements of percentiles. We also present a set of priors proposed by Singpurwala assuming a truncated normal prior distribution for the median of distribution and a gamma prior for the scale parameter. Next, we investigate the effects of these priors in the posterior estimates of the parameters of the Gompertz distribution. The Bayes estimates are computed using Markov Chain Monte Carlo (MCMC) algorithm. An extensive numerical simulation is carried out to evaluate the performance of the maximum likelihood estimates and Bayes estimates based on bias, mean-squared error and coverage probabilities. Finally, a real data set have been analyzed for illustrative purposes.en
dc.description.affiliationDepartamento de Estatística Faculdade de Ciências e Tecnologia Universidade Estadual Paulista/UNESP Rua Roberto Simonsen, 305, Centro Educacional
dc.description.affiliationDepartment of Statistics St. Anthony’s College, Bomfyle road, East Khasi Hills
dc.description.affiliationUnespDepartamento de Estatística Faculdade de Ciências e Tecnologia Universidade Estadual Paulista/UNESP Rua Roberto Simonsen, 305, Centro Educacional
dc.format.extent2643-2661
dc.identifierhttp://dx.doi.org/10.1590/0001-3765201820171040
dc.identifier.citationAnais da Academia Brasileira de Ciencias, v. 90, n. 3, p. 2643-2661, 2018.
dc.identifier.doi10.1590/0001-3765201820171040
dc.identifier.fileS0001-37652018000602643.pdf
dc.identifier.issn1678-2690
dc.identifier.issn0001-3765
dc.identifier.lattes1621269552366697
dc.identifier.orcid0000-0002-2445-0407
dc.identifier.scieloS0001-37652018000602643
dc.identifier.scopus2-s2.0-85054562013
dc.identifier.urihttp://hdl.handle.net/11449/188175
dc.language.isoeng
dc.relation.ispartofAnais da Academia Brasileira de Ciencias
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectElicitation
dc.subjectGompertz distribution
dc.subjectJeffreys prior
dc.subjectMaximal data information prior
dc.subjectObjective prior
dc.subjectSubjective prior
dc.titleObjective and subjective prior distributions for the gompertz distributionen
dc.typeArtigo
unesp.author.lattes1621269552366697[1]
unesp.author.orcid0000-0002-2445-0407[1]

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