Highest posterior density regions with approximate frequentist validity: The role of data-dependent priors

In Hong Chang and Rahul Mukerjee

Statistics & Probability Letters, 2010, vol. 80, issue 23-24, 1791-1797

Abstract: For the general multiparameter case, we consider the problem of ensuring frequentist validity of highest posterior density regions with margin of error o(n-1), where n is the sample size. The role of data-dependent priors is investigated and it is seen that the resulting probability matching condition readily allows solutions, in contrast to what happens with data-free priors. Moreover, use of data-dependent priors is seen to be helpful even for models, such as mixture models, where closed form expressions for the expected information elements do not exist.

Keywords: Mixture; model; Observed; information; Probability; matching; prior; Shrinkage; argument (search for similar items in EconPapers)
Date: 2010
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