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.
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