Frequentist coverage of adaptive nonparametric Bayesian credible sets

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

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

56 Citaten (Scopus)
100 Downloads (Pure)


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.
Originele taal-2Engels
Pagina's (van-tot)1391-1428
Aantal pagina's38
TijdschriftThe Annals of Statistics
Nummer van het tijdschrift4
StatusGepubliceerd - 2015

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