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Wavelet thresholding for nonnecessarily Gaussian noise: functionality

Research paper by R. Averkamp, C. Houdré

Indexed on: 11 Feb '06Published on: 11 Feb '06Published in: Mathematics - Statistics



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.