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Statistics > Methodology

arXiv:2204.07544 (stat)
[Submitted on 15 Apr 2022]

Title:Gamma-Minimax Wavelet Shrinkage with Three-Point Priors

Authors:Dixon Vimalajeewa, Brani Vidakovic
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Abstract:In this paper we propose a method for wavelet denoising of signals contaminated with Gaussian noise when prior information about the $L^2$-energy of the signal is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose a simple, level dependent shrinkage rules that turn out to be
$\Gamma$-minimax for a suitable class of priors.
The proposed methodology is particularly well suited in denoising tasks when the signal-to-noise ratio is low, which is illustrated by simulations on the battery of standard test functions. Comparison to some standardly used wavelet shrinkage methods is provided.
Comments: 21 pages, 12 figures, 1 table
Subjects: Methodology (stat.ME); Computation (stat.CO)
Cite as: arXiv:2204.07544 [stat.ME]
  (or arXiv:2204.07544v1 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2204.07544

Submission history

From: Brani Vidakovic [view email]
[v1] Fri, 15 Apr 2022 17:03:10 UTC (795 KB)
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