On optimality of Bayesian testimation in the normal means problem

Felix Abramovich, Vadim Grinshtein, Marianna Pensky

The Annals of Statistics · 2007 · Volume 35 · Issue 5

Abstract

We consider a problem of recovering a high-dimensional vector μ observed in white noise, where the unknown vector μ is assumed to be sparse. The objective of the paper is to develop a Bayesian formalism which gives rise to a family of l0-type penalties. The penalties are associated with various choices of the prior distributions πn(⋅) on the number of nonzero entries of μ and, hence, are easy to interpret. The resulting Bayesian estimators lead to a general thresholding rule which accommodates many of the known thresholding and model selection procedures as particular cases corresponding to specific choices of πn(⋅). Furthermore, they achieve optimality in a rather general setting under very mild conditions on the prior. We also specify the class of priors πn(⋅) for which the resulting estimator is adaptively optimal (in the minimax sense) for a wide range of sparse sequences and consider several examples of such priors.

Keywords

62C10, 62C20, 62G05, Adaptivity, complexity penalty, maximum a posteriori rule, minimax estimation, sequence estimation, Sparsity, thresholding

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