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.


Reference manual

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1.4-12 by Peter Carbonetto, a month ago


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