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Empirical Bayes selection of wavelet thresholds

Johnstone, Iain; Silverman, Bernard W


This paper explores a class of empirical Bayes methods for level-dependent threshold selection in wavelet shrinkage. The prior considered for each wavelet coefficient is a mixture of an atom of probability at zero and a heavy-tailed density. The mixing weight, or sparsity parameter, for each level of the transform is chosen by marginal maximum likelihood. If estimation is carried out using the posterior median, this is a random thresholding procedure; the estimation can also be carried out...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
Source: Annals of Statistics
DOI: 10.1214/009053605000000345


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