Open Access
2016 Conditions for posterior contraction in the sparse normal means problem
S.L. van der Pas, J.-B. Salomond, J. Schmidt-Hieber
Electron. J. Statist. 10(1): 976-1000 (2016). DOI: 10.1214/16-EJS1130


The first Bayesian results for the sparse normal means problem were proven for spike-and-slab priors. However, these priors are less convenient from a computational point of view. In the meanwhile, a large number of continuous shrinkage priors has been proposed. Many of these shrinkage priors can be written as a scale mixture of normals, which makes them particularly easy to implement. We propose general conditions on the prior on the local variance in scale mixtures of normals, such that posterior contraction at the minimax rate is assured. The conditions require tails at least as heavy as Laplace, but not too heavy, and a large amount of mass around zero relative to the tails, more so as the sparsity increases. These conditions give some general guidelines for choosing a shrinkage prior for estimation under a nearly black sparsity assumption. We verify these conditions for the class of priors considered in [12], which includes the horseshoe and the normal-exponential gamma priors, and for the horseshoe+, the inverse-Gaussian prior, the normal-gamma prior, and the spike-and-slab Lasso, and thus extend the number of shrinkage priors which are known to lead to posterior contraction at the minimax estimation rate.


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S.L. van der Pas. J.-B. Salomond. J. Schmidt-Hieber. "Conditions for posterior contraction in the sparse normal means problem." Electron. J. Statist. 10 (1) 976 - 1000, 2016.


Received: 1 October 2015; Published: 2016
First available in Project Euclid: 12 April 2016

zbMATH: 1343.62012
MathSciNet: MR3486423
Digital Object Identifier: 10.1214/16-EJS1130

Primary: 62F15
Secondary: 62G20

Keywords: Bayesian inference , frequentist Bayes , horseshoe , horseshoe+ , nearly black vectors , normal means problem , posterior contraction , shrinkage priors , Sparsity

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.10 • No. 1 • 2016
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