A Modification for Bayesian Credible Intervals
Tonglin Zhang
Abstract
Tonglin Zhang
Abstract
In Bayesian analysis, people usually report the highest posterior density (HPD) credible interval as an interval estimate of an unknown parameter. However, when the unknown parameter is the nonnegative normal mean, the Bayesian HPD credible interval under the uniform prior has quite a low minimum frequentist coverage probability. To enhance the minimum frequentist coverage probability of a credible interval, I propose a new method of reporting the Bayesian credible interval. Numerical results show that the new reported credible interval has a much higher minimum frequentist coverage probability than the HPD credible interval.
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In Bayesian analysis, people usually report the highest posterior density (HPD) credible interval as an interval estimate of an unknown parameter. However, when the unknown parameter is the nonnegative normal mean, the Bayesian HPD credible interval under the uniform prior has quite a low minimum frequentist coverage probability. To enhance the minimum frequentist coverage probability of a credible interval, I propose a new method of reporting the Bayesian credible interval. Numerical results show that the new reported credible interval has a much higher minimum frequentist coverage probability than the HPD credible interval.
Key concepts: Frequentist inference, Credible interval, Interval (graph theory), Bayesian probability, Coverage probability, Statistics, Confidence interval, Mathematics