Frequentist coverage of adaptive nonparametric Bayesian credible sets

B.T. Szabó, A.W. Vaart, van der, J.H. Zanten, van

Research output: Contribution to journalArticleAcademicpeer-review

90 Citations (Scopus)
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We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting. We consider a scale of priors of varying regularity and choose the regularity by an empirical Bayes method. Next we consider a central set of prescribed posterior probability in the posterior distribution of the chosen regularity. We show that such an adaptive Bayes credible set gives correct uncertainty quantification of "polished tail" parameters, in the sense of high probability of coverage of such parameters. On the negative side, we show by theory and example that adaptation of the prior necessarily leads to gross and haphazard uncertainty quantification for some true parameters that are still within the hyperrectangle regularity scale.
Original languageEnglish
Pages (from-to)1391-1428
Number of pages38
JournalThe Annals of Statistics
Issue number4
Publication statusPublished - 2015


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