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Empirical Bayes approach to block wavelet function estimation

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  • Abramovich, Felix
  • Besbeas, Panagiotis
  • Sapatinas, Theofanis

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  • Abramovich, Felix & Besbeas, Panagiotis & Sapatinas, Theofanis, 2002. "Empirical Bayes approach to block wavelet function estimation," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 435-451, June.
    • Handle: RePEc:eee:csdana:v:39:y:2002:i:4:p:435-451
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    References listed on IDEAS

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    1. Brani Vidakovic, 1999. "Linear Versus Nonlinear Rules for Mixture Normal Priors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(1), pages 111-124, March.
    2. Merlise Clyde & Edward I. George, 2000. "Flexible empirical Bayes estimation for wavelets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 681-698.
    3. M. Vannucci & F. Corradi, 1999. "Covariance structure of wavelet coefficients: theory and models in a Bayesian perspective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 971-986.
    4. A. Antoniadis, 1997. "Wavelets in statistics: A review," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 97-130, August.
    5. F. Abramovich & T. Sapatinas & B. W. Silverman, 1998. "Wavelet thresholding via a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(4), pages 725-749.
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    Cited by:

    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    2. Fryzlewicz, Piotr, 2007. "Bivariate hard thresholding in wavelet function estimation," LSE Research Online Documents on Economics 25219, London School of Economics and Political Science, LSE Library.
    3. Serban, Nicoleta, 2010. "Noise reduction for enhanced component identification in multi-dimensional biomolecular NMR studies," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1051-1065, April.
    4. Reményi, Norbert & Vidakovic, Brani, 2013. "Λ-neighborhood wavelet shrinkage," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 404-416.
    5. Abdallah Abu Abdallah & Mousa Mohammad Abdullah Saleh & Sadam Al-Wadi & Firas Al Rawashdeh, 2019. "Improving the Estimation Accuracy Based on Wavelet Transform," Journal of Social Sciences (COES&RJ-JSS), , vol. 8(4), pages 544-557, October.
    6. Sam Efromovich, 2004. "Analysis of blockwise shrinkage wavelet estimates via lower bounds for no-signal setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(2), pages 205-223, June.
    7. Vincent Rivoirard, 2004. "Thresholding procedure with priors based on Pareto distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(1), pages 213-246, June.
    8. Wang, Xue & Walker, Stephen G., 2010. "A penalised data-driven block shrinkage approach to empirical Bayes wavelet estimation," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 990-996, June.
    9. Stuart Barber & Guy P. Nason, 2004. "Real nonparametric regression using complex wavelets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 927-939, November.

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