Bayesian inference for two-parameter gamma distribution assuming different noninformative priors
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Data
2013-12-01
Autores
Moala, Fernando Antonio [UNESP]
Ramos, Pedro Luiz [UNESP]
Achcar, Jorge Alberto
Título da Revista
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Título de Volume
Editor
Univ Nac Colombia, Dept Estadistica
Resumo
In 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.
Descrição
Palavras-chave
Gamma distribution, noninformative prior, copula, conjugate, Jeffreys prior, reference, MDIP, orthogonal, MCMC
Como citar
Revista Colombiana de Estadistica. Bogota Dc: Univ Nac Colombia, Dept Estadistica, v. 36, n. 2, p. 321-338, 2013.