EBayesThresh

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 73 articles )

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  1. 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)
  2. Park, Chun Gun; Kim, Inyoung: Efficient resolution and basis functions selection in wavelet regression (2015)
  3. Lian, Heng: Adaptive rates of contraction of posterior distributions in Bayesian wavelet regression (2014)
  4. Martin, Ryan; Walker, Stephen G.: Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector (2014)
  5. Neville, Sarah E.; Ormerod, John T.; Wand, M.P.: Mean field variational Bayes for continuous sparse signal shrinkage: pitfalls and remedies (2014)
  6. Pal, Subhadip; Khare, Kshitij: Geometric ergodicity for Bayesian shrinkage models (2014)
  7. Park, Junyong: Shrinkage estimator in normal mean vector estimation based on conditional maximum likelihood estimators (2014)
  8. Frommlet, Florian; Bogdan, Małgorzata: Some optimality properties of FDR controlling rules under sparsity (2013)
  9. Jiang, Wenhua; Zhang, Cun-Hui: A nonparametric empirical Bayes approach to adaptive minimax estimation (2013)
  10. Strawderman, Robert L.; Wells, Martin T.; Schifano, Elizabeth D.: Hierarchical Bayes, maximum a posteriori estimators, and minimax concave penalized likelihood estimation (2013)
  11. Szabó, B.T.; van der Vaart, A.W.; van Zanten, J.H.: Empirical Bayes scaling of Gaussian priors in the white noise model (2013)
  12. Altaher, Alsaidi M.; Ismail, Mohd Tahir: Robust wavelet estimation to eliminate simultaneously the effects of boundary problems, outliers, and correlated noise (2012)
  13. Altaher, Alsaidi M.; Ismail, Mohd Tahir: Local polynomial wavelet regression with missing at random (2012)
  14. Castillo, Ismaël; van der Vaart, Aad: Needles and straw in a haystack: posterior concentration for possibly sparse sequences (2012)
  15. Chrétien, Stéphane; Hero, Alfred; Perdry, Hervé: Space alternating penalized Kullback proximal point algorithms for maximizing likelihood with nondifferentiable penalty (2012)
  16. Dalalyan, Arnak S.; Tsybakov, Alexandre B.: Mirror averaging with sparsity priors (2012)
  17. Dalalyan, A.S.; Tsybakov, A.B.: Sparse regression learning by aggregation and Langevin Monte-Carlo (2012)
  18. Fryzlewicz, Piotr: Time-threshold maps: using information from wavelet reconstructions with all threshold values simultaneously (2012)
  19. Polson, Nicholas G.; Scott, James G.: Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors (2012)
  20. Yue, Yu Ryan; Speckman, Paul L.; Sun, Dongchu: Priors for Bayesian adaptive spline smoothing (2012)

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