EbayesThresh library is a collection of MATLAB™ scripts that complements the paper ”Needles and straw in haystacks: Empirical Bayes approaches to thresholding a possibly sparse sequence” and ”Empirical Bayes selection of wavelet thresholds” by Iain M. Johnstone and Bernard W. Silverman, submitted for publication 2002. A paper giving a general description of the software and some details both of the general methodology and of some specific technical matters is available here. The scripts in this library are a translation of the corresponding R package or S-PLUS library. The ebayesthresh_wavelet function applies the approach to wavelet transforms obtained with the WAVELAB matlab toolbox developed at Stanford by Buckheit, Chen, Donoho, Johnstone & Scargle (1995). If wavelet transforms are obtained using other software, the routine will not be applicable directly, but should still provide a model for the user to write their own wavelet smoothing routine making use of the function ebayesthresh. The software may be downloaded and used freely for academic purposes, provided its use is acknowledged. Commercial use is not allowed without the permission of the authors. Please bring any problems or errors to the author’s attention. The entire MATLAB source code, in compressed zip form, is available for download from: ..

References in zbMATH (referenced in 90 articles )

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  1. Ročková, Veronika: Bayesian estimation of sparse signals with a continuous spike-and-slab prior (2018)
  2. Belitser, Eduard; Nurushev, Nurzhan: Local posterior concentration rate for multilevel sparse sequences (2017)
  3. Jang, Dongik; Kim, Donghoh; Kim, Kyungmee O.: Multiscale representation for irregularly spaced data (2017)
  4. Sanyal, Nilotpal; Ferreira, Marco A. R.: Bayesian wavelet analysis using nonlocal priors with an application to fMRI analysis (2017)
  5. Wang, Charlotte; Ruggeri, Fabrizio; Hsiao, Chuhsing K.; Argiento, Raffaele: Bayesian nonparametric clustering and association studies for candidate SNP observations (2017)
  6. Bhattacharya, Anirban; Dunson, David B.; Pati, Debdeep; Pillai, Natesh S.: Sub-optimality of some continuous shrinkage priors (2016)
  7. Knapik, B.T.; Szabó, B.T.; van der Vaart, A.W.; van Zanten, J.H.: Bayes procedures for adaptive inference in inverse problems for the white noise model (2016)
  8. van der Pas, S.L.; Salomond, J.-B.; Schmidt-Hieber, J.: Conditions for posterior contraction in the sparse normal means problem (2016)
  9. Castillo, Ismaël; Schmidt-Hieber, Johannes; van der Vaart, Aad: Bayesian linear regression with sparse priors (2015)
  10. Kabaila, Paul; Dharmarathne, Gayan: A comparison of Bayesian and frequentist interval estimators in regression that utilize uncertain prior information (2015)
  11. Lepski, Oleg: Adaptive estimation over anisotropic functional classes via oracle approach (2015)
  12. Park, Chun Gun; Kim, Inyoung: Efficient resolution and basis functions selection in wavelet regression (2015)
  13. Pungpapong, Vitara; Zhang, Min; Zhang, Dabao: Selecting massive variables using an iterated conditional modes/medians algorithm (2015)
  14. Xu, Xiaofan; Ghosh, Malay: Bayesian variable selection and estimation for group Lasso (2015)
  15. Zhou, Yi-Hui; Marron, J.S.: High dimension low sample size asymptotics of robust PCA (2015)
  16. Lian, Heng: Adaptive rates of contraction of posterior distributions in Bayesian wavelet regression (2014)
  17. Martin, Ryan; Walker, Stephen G.: Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector (2014)
  18. Neville, Sarah E.; Ormerod, John T.; Wand, M.P.: Mean field variational Bayes for continuous sparse signal shrinkage: pitfalls and remedies (2014)
  19. Pal, Subhadip; Khare, Kshitij: Geometric ergodicity for Bayesian shrinkage models (2014)
  20. Park, Junyong: Shrinkage estimator in normal mean vector estimation based on conditional maximum likelihood estimators (2014)

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