AbstractWavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wavelet coefficients. Such simple estimators are now well explored and widely used in wavelet-based nonparametrics. Results of Tao (1996, Appl. Comput. Harmon. Anal.3, 384–387) demonstrated that hard and soft thresholding shrinkage estimators absolutely converge almost everywhere to the original function when the threshold value goes to zero. Such natural and intuitive behavior of threshold estimators is expected, yet this result does not translate to the Fourier expansions. In this paper we show that almost everywhere convergence of shrinkage estimators holds for a range of shrinkage rules, not necessarily thresholding, subject to some mild tech...
With this article we first like to give a brief review on wavelet thresholding methods in non-Gaussi...
International audienceThis note is devoted to an analysis of the so-called peeling algorithm in wave...
AbstractWe consider the abstract problem of approximating a function ψ0∈L1(Rd)∩L2(Rd) given only noi...
AbstractWavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wave...
AbstractWavelet shrinkage is a strategy to obtain a nonlinear approximation to a given signal. The s...
International audienceThis work addresses the unification of some basic functions and thresholds use...
AbstractWavelet shrinkage is a strategy to obtain a nonlinear approximation to a given signal and is...
Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-p...
International audienceWavelet transforms are said to be sparse in that they represent smooth andpiec...
Standard wavelet shrinkage procedures for nonparametric regression are restricted to equispaced samp...
Finding a sparse representation of a possibly noisy signal is a common problem in signal representa...
Accepted for publication in Journal of the American Statistical Association. The definitive version ...
AbstractIn the paper minimax rates of convergence for wavelet estimators are studied. The estimators...
Finding a sparse representation of a possibly noisy signal can be modeled as a variational minimizat...
With this article we first like to give a brief review on wavelet thresholding methods in non-Gaussi...
With this article we first like to give a brief review on wavelet thresholding methods in non-Gaussi...
International audienceThis note is devoted to an analysis of the so-called peeling algorithm in wave...
AbstractWe consider the abstract problem of approximating a function ψ0∈L1(Rd)∩L2(Rd) given only noi...
AbstractWavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wave...
AbstractWavelet shrinkage is a strategy to obtain a nonlinear approximation to a given signal. The s...
International audienceThis work addresses the unification of some basic functions and thresholds use...
AbstractWavelet shrinkage is a strategy to obtain a nonlinear approximation to a given signal and is...
Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-p...
International audienceWavelet transforms are said to be sparse in that they represent smooth andpiec...
Standard wavelet shrinkage procedures for nonparametric regression are restricted to equispaced samp...
Finding a sparse representation of a possibly noisy signal is a common problem in signal representa...
Accepted for publication in Journal of the American Statistical Association. The definitive version ...
AbstractIn the paper minimax rates of convergence for wavelet estimators are studied. The estimators...
Finding a sparse representation of a possibly noisy signal can be modeled as a variational minimizat...
With this article we first like to give a brief review on wavelet thresholding methods in non-Gaussi...
With this article we first like to give a brief review on wavelet thresholding methods in non-Gaussi...
International audienceThis note is devoted to an analysis of the so-called peeling algorithm in wave...
AbstractWe consider the abstract problem of approximating a function ψ0∈L1(Rd)∩L2(Rd) given only noi...