EbayesThresh: Empirical Bayes Thresholding and Related Methods

This package carries out Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.

Version: 1.3.2
Enhances: wavethresh
Published: 2012-10-29
Author: Bernard W. Silverman
Maintainer: Ludger Evers <ludger at stats.gla.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Bayesian
CRAN checks: EbayesThresh results

Downloads:

Reference manual: EbayesThresh.pdf
Package source: EbayesThresh_1.3.2.tar.gz
Windows binaries: r-devel: EbayesThresh_1.3.2.zip, r-release: EbayesThresh_1.3.2.zip, r-oldrel: EbayesThresh_1.3.2.zip
OS X Snow Leopard binaries: r-release: EbayesThresh_1.3.2.tgz, r-oldrel: EbayesThresh_1.3.2.tgz
OS X Mavericks binaries: r-release: EbayesThresh_1.3.2.tgz
Old sources: EbayesThresh archive

Reverse dependencies:

Reverse depends: adlift, CVThresh, nlt