Bayesian Analysis

Uncertainty Quantification for the Horseshoe

Stéphanie van der Pas, Botond Szabó, and Aad van der Vaart

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We investigate the credible sets and marginal credible intervals resulting from the horseshoe prior in the sparse multivariate normal means model. We do so in an adaptive setting without assuming knowledge of the sparsity level (number of signals). We consider both the hierarchical Bayes method of putting a prior on the unknown sparsity level and the empirical Bayes method with the sparsity level estimated by maximum marginal likelihood. We show that credible balls and marginal credible intervals have good frequentist coverage and optimal size if the sparsity level of the prior is set correctly. By general theory honest confidence sets cannot adapt in size to an unknown sparsity level. Accordingly the hierarchical and empirical Bayes credible sets based on the horseshoe prior are not honest over the full parameter space. We show that this is due to over-shrinkage for certain parameters and characterise the set of parameters for which credible balls and marginal credible intervals do give correct uncertainty quantification. In particular we show that the fraction of false discoveries by the marginal Bayesian procedure is controlled by a correct choice of cut-off.

Article information

Bayesian Anal. (2017), 29 pages.

First available in Project Euclid: 1 September 2017

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Digital Object Identifier

Primary: 62G15: Tolerance and confidence regions
Secondary: 62F15: Bayesian inference

credible sets horseshoe sparsity nearly black vectors normal means problem frequentist Bayes

Creative Commons Attribution 4.0 International License.


van der Pas, Stéphanie; Szabó, Botond; van der Vaart, Aad. Uncertainty Quantification for the Horseshoe. Bayesian Anal., advance publication, 1 September 2017. doi: 10.1214/17-BA1065.

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