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 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.

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Jason Willwerscheid, Matthew Stephens: ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families (2021) arXiv
  2. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  3. Xing, Zhengrong; Carbonetto, Peter; Stephens, Matthew: Flexible signal denoising via flexible empirical Bayes shrinkage (2021)
  4. Ludger Evers; Tim Heaton: Locally Adaptive Tree-Based Thresholding Using the treethresh Package in R (2017) not zbMATH
  5. McGinnity, K.; Varbanov, R.; Chicken, E.: Cross-validated wavelet block thresholding for non-Gaussian errors (2017)
  6. Roger Koenker; Jiaying Gu: REBayes: An R Package for Empirical Bayes Mixture Methods (2017) not zbMATH
  7. Pungpapong, Vitara; Zhang, Min; Zhang, Dabao: Selecting massive variables using an iterated conditional modes/medians algorithm (2015)
  8. Park, Junyong: Shrinkage estimator in normal mean vector estimation based on conditional maximum likelihood estimators (2014)
  9. Frommlet, Florian; Bogdan, Małgorzata: Some optimality properties of FDR controlling rules under sparsity (2013)
  10. Wolpert, Robert L.; Clyde, Merlise A.; Tu, Chong: Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels (2011)
  11. Zhang, Dabao; Lin, Yanzhu; Zhang, Min: Penalized orthogonal-components regression for large (p) small (n) data (2009)
  12. Fryzlewicz, Piotr: Data-driven wavelet-Fisz methodology for nonparametric function estimation (2008)
  13. Braak, Cajo J. F. Ter: Bayesian sigmoid shrinkage with improper variance priors and an application to wavelet denoising (2006)
  14. Donghoh Kim and Hee-Seok Oh: CVTresh: R Package for Level-Dependent Cross-Validation Thresholding (2006) not zbMATH
  15. Iain Johnstone; Bernard Silverman: EbayesThresh: R Programs for Empirical Bayes Thresholding (2005) not zbMATH
  16. Johnstone, Iain M.; Silverman, Bernard W.: Empirical Bayes selection of wavelet thresholds (2005)
  17. Johnstone, Iain M.; Silverman, Bernhard W.: Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences (2004)