Asymptotic Inference for Mixture Models by Using Data-Dependent Priors
Larry H. Wasserman · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2000
Summary For certain mixture models, improper priors are undesirable because they yield improper posteriors. However, proper priors may be undesirable because they require subjective input. We propose the use of specially chosen data-dependent priors. We show that, in some cases, data-dependent priors are the only priors that produce intervals with second-order correct frequentist coverage. The resulting posterior also has another interpretation: it is the product of a fixed prior and a pseudolikelihood.