Bayesian Estimation of Inverse Gaussian Distribution

Abstract

In this paper we consider Bayesian estimation for the parameters of inverse Gaussian distribution. Our emphasis is on Markov Chain Monte Carlo methods. We provide complete implementation of the Gibbs sampler algorithm. Assuming an informative prior, Bayes estimates are computed using the output of the Gibbs sampler and also from Lindley's approximation method. Maximum Likelihood and Uniformly Minimum Variance Unbiased estimates are obtained as well. We also compute Highest Posterior Density credible intervals, exact confidence intervals as well as "percentile" and "percentile-t" bootstrap approximations to the exact intervals. A simulation study was conducted to compare the long-run performance of the various point and interval estimation methods considered. One real data illustration has been provided which brings out some salient features of sampling-based approach to inference.