Article

A penalised data-driven block shrinkage approach to empirical Bayes wavelet estimation

Statistics [?] Probability Letters (impact factor: 0.5). 01/2010; 80(11-12):990-996. pp.990-996
Source: RePEc

ABSTRACT In this paper we propose a simple Bayesian block wavelet shrinkage method for estimating an unknown function in the presence of Gaussian noise. A data-driven procedure which can adaptively choose the block size and the shrinkage level at each resolution level is provided. The asymptotic property of the proposed method, BBN (Bayesian BlockNorm shrinkage), is investigated in the Besov sequence space. The numerical performance and comparisons with some of existing wavelet denoising methods show that the new method can achieve good performance but with the least computational time.

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Keywords

asymptotic property
 
Bayesian BlockNorm shrinkage
 
Besov sequence space
 
block size
 
computational time
 
data-driven procedure
 
good performance
 
simple Bayesian block wavelet shrinkage method
 
unknown function
 
wavelet denoising methods
 

Xue Wang