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

Wang, Xue, Walker, Stephen G. (2010) A penalised data-driven block shrinkage approach to empirical Bayes wavelet estimation. Statistics and Probability Letters, 80 (11-12). pp. 990-996. ISSN 0167-7152. (doi:10.1016/j.spl.2010.02.013)

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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.

Item Type: Article
DOI/Identification number: 10.1016/j.spl.2010.02.013
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Xue Wang
Date Deposited: 13 Dec 2013 09:15 UTC
Last Modified: 29 May 2019 11:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37605 (The current URI for this page, for reference purposes)

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