EbayesThresh
swMATH ID:  11103 
Software Authors:  Bernard W. Silverman 
Description:  R package 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 heavytailed 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. 
Homepage:  http://cran.rproject.org/web/packages/EbayesThresh/index.html 
Source Code:  https://github.com/cran/EbayesThresh 
Dependencies:  R 
Related Software:  EBayesThresh; R; wavethresh; Mosek; REBayes; Rmosek; Waveslim; GitHub; ebnm; flashier; microbenchmark; trust; deconvolveR; mixsqp; ashr; horseshoe; png; colorRamps; treethresh; CVTresh 
Cited in:  12 Publications 
Standard Articles
1 Publication describing the Software  Year 


all
top 5
Cited by 20 Authors
Cited in 5 Serials
4  Electronic Journal of Statistics 
3  The Annals of Statistics 
2  Computational Statistics and Data Analysis 
2  Journal of Machine Learning Research (JMLR) 
1  Statistics & Probability Letters 
Cited in 4 Fields
10  Statistics (62XX) 
3  Numerical analysis (65XX) 
2  Computer science (68XX) 
1  Harmonic analysis on Euclidean spaces (42XX) 