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: 1.4-12 Imports: stats, wavethresh Suggests: testthat, knitr, rmarkdown, dplyr, ggplot2 Published: 2017-08-08 DOI: 10.32614/CRAN.package.EbayesThresh Author: Bernard W. Silverman [aut], Ludger Evers [aut], Kan Xu [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut] Maintainer: Peter Carbonetto <peter.carbonetto at gmail.com> BugReports: https://github.com/stephenslab/EbayesThresh/issues License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://github.com/stephenslab/EbayesThresh NeedsCompilation: no In views: Bayesian CRAN checks: EbayesThresh results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=EbayesThresh to link to this page.
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