BAYESIAN INFERENCES FOR THE BIRNBAUM-SAUNDERS SPECIAL-CASE DISTRIBUTION
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Abstract
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.
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NAKAMURA, L. R., LEANDRO, R. A., & VILLEGAS, C. (2016). BAYESIAN INFERENCES FOR THE BIRNBAUM-SAUNDERS SPECIAL-CASE DISTRIBUTION. Brazilian Journal of Biometrics, 34(2), 365–378. Retrieved from http://www.biometria.ufla.br/index.php/BBJ/article/view/146
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