Abstract
With reference to a wide class of empirical and related likelihoods, we study priors which ensure approximate frequentist validity of the posterior quantiles of a general parametric function. It is seen that no data-free prior entails such frequentist validity but, at least for the usual empirical likelihood, a data-dependent prior serves the purpose. Accounting for the nonlinearity of the parametric function of interest requires special attention in the derivation. A simulation study is seen to provide support, in finite samples, to our asymptotic results.
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