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October 2005 Wavelet thresholding for nonnecessarily Gaussian noise: Functionality
R. Averkamp, C. Houdré
Ann. Statist. 33(5): 2164-2193 (October 2005). DOI: 10.1214/009053605000000471

Abstract

For signals belonging to balls in smoothness classes and noise with enough moments, the asymptotic behavior of the minimax quadratic risk among soft-threshold estimates is investigated. In turn, these results, combined with a median filtering method, lead to asymptotics for denoising heavy tails via wavelet thresholding. Some further comparisons of wavelet thresholding and of kernel estimators are also briefly discussed.

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R. Averkamp. C. Houdré. "Wavelet thresholding for nonnecessarily Gaussian noise: Functionality." Ann. Statist. 33 (5) 2164 - 2193, October 2005. https://doi.org/10.1214/009053605000000471

Information

Published: October 2005
First available in Project Euclid: 25 November 2005

zbMATH: 1086.62043
MathSciNet: MR2211083
Digital Object Identifier: 10.1214/009053605000000471

Subjects:
Primary: 62C20 , 62G07
Secondary: 41A25 , 60G70

Keywords: minimax , thresholding , Wavelets

Rights: Copyright © 2005 Institute of Mathematical Statistics

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Vol.33 • No. 5 • October 2005
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