Open Access
October2000 Bayesian prediction with approximate frequentist validity
Gauri Sankar Datta, Malay Ghosh, Rahul Mukerjee, Trevor J. Sweeting
Ann. Statist. 28(5): 1414-1426 (October2000). DOI: 10.1214/aos/1015957400


We characterize priors which asymptotically match the posterior coverage probability of a Bayesian prediction region with the corresponding frequentist coverage probability. This is done considering both posterior quantiles and highest predictive density regions with reference to a future observation. The resulting priors are shown to be invariant under reparameterization. The role of Jeffreys’ prior in this regard is also investigated. It is further shown that, for any given prior, it may be possible to choose an interval whose Bayesian predictive and frequentist coverage probabilities are asymptotically matched.


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Gauri Sankar Datta. Malay Ghosh. Rahul Mukerjee. Trevor J. Sweeting. "Bayesian prediction with approximate frequentist validity." Ann. Statist. 28 (5) 1414 - 1426, October2000.


Published: October2000
First available in Project Euclid: 12 March 2002

zbMATH: 1105.62312
MathSciNet: MR1805790
Digital Object Identifier: 10.1214/aos/1015957400

Primary: 62C10 , 62F15

Keywords: Highest predictive density region , Jeffreys' prior , noninformative prior , posterior quantile , prediction interval

Rights: Copyright © 2000 Institute of Mathematical Statistics

Vol.28 • No. 5 • October2000
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