Empirical Bayes Thresholding and Related Methods

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.


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Reference manual

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install.packages("EbayesThresh")

1.4-12 by Peter Carbonetto, a month ago


https://github.com/stephenslab/EbayesThresh


Report a bug at https://github.com/stephenslab/EbayesThresh/issues


Browse source code at https://github.com/cran/EbayesThresh


Authors: Bernard W. Silverman [aut], Ludger Evers [aut], Kan Xu [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut]


Documentation:   PDF Manual  


Task views: Bayesian Inference


GPL (>= 2) license


Imports stats, wavethresh

Suggests testthat, knitr, rmarkdown, dplyr, ggplot2


Imported by icmm.

Depended on by CVThresh, adlift, nlt, treethresh.


See at CRAN