Geometric approach for finding HPD-credible sets with applications

摘要

An algorithm, based on computational geometry, for computing optimum Bayes credible intervals is presented. A credible interval is the Bayesian version of a classical confidence interval estimate, except it does not require the existence of a pivotal quantity, and it is easier to interpret than the classical confidence interval. For the problem of confidence interval estimation, the standard technique in applied statistics is the use of equal-tailed confidence intervals. The algorithm has been implemented in Java and its performance is tested using Monte Carlo simulation on environmental data from Superfund sites. The results show that the credible interval sets generated by the proposed algorithm is significantly shorter than the interval computed by the standard equal-tailed method.