General empirical Bayes wavelet methods and exactly adaptive minimax estimation

Details General empirical Bayes wavelet methods and exactly adaptive minimax estimation General empirical Bayes wavelet methods and exactly adaptive minimax estimation

2005-04-25

arxiv.org/abs/math/0504501v1

Annals of Statistics 2005, Vol. 33, No. 1, 54-100

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.1214/009053604000000995 in the Annals of Statistics (http://www.imstat.org/aos/) by the Inst

Scientific paper

10.1214/009053604000000995

In many statistical problems, stochastic signals can be represented as a sequence of noisy wavelet coefficients. In this paper, we develop general empirical Bayes methods for the estimation of true signal. Our estimators approximate certain oracle separable rules and achieve adaptation to ideal risks and exact minimax risks in broad collections of classes of signals. In particular, our estimators are uniformly adaptive to the minimum risk of separable estimators and the exact minimax risks simultaneously in Besov balls of all smoothness and shape indices, and they are uniformly superefficient in convergence rates in all compact sets in Besov spaces with a finite secondary shape parameter. Furthermore, in classes nested between Besov balls of the same smoothness index, our estimators dominate threshold and James-Stein estimators within an infinitesimal fraction of the minimax risks. More general block empirical Bayes estimators are developed. Both white noise with drift and nonparametric regression are considered.

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