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.

Install the latest version of this package by entering the following in R:
AuthorBernard W. Silverman
Date of publication2012-10-29 08:57:00
MaintainerLudger Evers <ludger@stats.gla.ac.uk>
LicenseGPL (>= 2)

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beta.cauchy Man page
beta.laplace Man page
cauchy.medzero Man page
cauchy.threshzero Man page
ebayesthresh Man page
ebayesthresh.wavelet Man page
ebayesthresh.wavelet.dwt Man page
ebayesthresh.wavelet.splus Man page
ebayesthresh.wavelet.wd Man page
isotone Man page
laplace.threshzero Man page
negloglik.laplace Man page
postmean Man page
postmean.cauchy Man page
postmean.laplace Man page
postmed Man page
postmed.cauchy Man page
postmed.laplace Man page
tfromw Man page
tfromx Man page
threshld Man page
vecbinsolv Man page
wandafromx Man page
wfromt Man page
wfromx Man page
wmonfromx Man page
zetafromx Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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