BAYESIAN INFERENCES FOR THE BIRNBAUM-SAUNDERS SPECIAL-CASE DISTRIBUTION - NAKAMURA, Luiz Ricardo; LEANDRO, Roseli Aparecida; VILLEGAS, Cristian (Brazilian Journal of Biometrics, 2016)
In this paper, we discuss the estimation of the Birnbaum-Saunders Special-Case (BS-SC) distribution through the Bayesian approach considering its parameters independents, assuming gamma priors for both of them. As the full posterior conditionals do not have closed forms we use the Metropolis-Hastings algorithm to generate samples from the joint posterior distribution. We present a simulation study proposing the Markov chain Monte Carlo (MCMC) method as a random number generator, considering the cases where the BS-SC distribution has symmetric and asymmetric shapes. An application related to ozone concentration is presented in this paper using the described methodology.