The horseshoe estimator: Posterior concentration around nearly black vectors

S. L. van der Pas, B. J. Kleijn, A. W. van der Vaart

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We consider the horseshoe estimator due to Carvalho, Polson and Scott (2010) for the multivariate normal mean model in the situation that the mean vector is sparse in the nearly black sense. We assume the fre- quentist framework where the data is generated according to a fixed mean vector. We show that if the number of nonzero parameters of the mean vector is known, the horseshoe estimator attains the minimax ℓ2 risk, pos- sibly up to a multiplicative constant. We provide conditions under which the horseshoe estimator combined with an empirical Bayes estimate of the number of nonzero means still yields the minimax risk. We furthermore prove an upper bound on the rate of contraction of the posterior distri- bution around the horseshoe estimator, and a lower bound on the poste- rior variance. These bounds indicate that the posterior distribution of the horseshoe prior may be more informative than that of other one-component priors, including the Lasso.

Original languageEnglish
Pages (from-to)2585-2618
Number of pages34
JournalElectronic Journal of Statistics
Volume8
DOIs
Publication statusPublished - 2015
Externally publishedYes

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