Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors - Rousseau, J; Szabo, B (Institute of Mathematical Statistics, 2020)
We investigate the frequentist coverage properties of (certain) Bayesian credible sets in a general, adaptive, nonparametric framework. It is well known that the construction of adaptive and honest confidence sets is not possible in general. To overcome this problem (in context of sieve type of priors), we introduce an extra assumption on the functional parameters, the so-called “general polished tail” condition. We then show that under standard assumptions, both the hierarchical and empirical Bayes methods, result in honest confidence sets for sieve type of priors in general settings and we characterize their size. We apply the derived abstract results to various examples, including the nonparametric regression model, density estimation using exponential families of priors, density estimation using histogram priors and the nonparametric classification model, for which we show that their size is near minimax adaptive with respect to the considered specific pseudometrics.