Wavelet thresholding for nonnecessarily Gaussian noise: functionality
统计理论
2016-08-16 v1 统计理论
摘要
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.
引用
@article{arxiv.math/0602241,
title = {Wavelet thresholding for nonnecessarily Gaussian noise: functionality},
author = {R. Averkamp and C. Houdré},
journal= {arXiv preprint arXiv:math/0602241},
year = {2016}
}
备注
Published at http://dx.doi.org/10.1214/009053605000000471 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)