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   -> Volume 3, Issue 17


Preprint: Nonlinear wavelet shrinkage with Bayes rules ...
 
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Brani Vidakovic (brani@isds.Duke.EDU)
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PostPosted: Mon Dec 02, 2002 1:27 pm    
Subject: Preprint: Nonlinear wavelet shrinkage with Bayes rules ...
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Preprint: Nonlinear wavelet shrinkage with Bayes rules ...

Manuscript Available.

Nonlinear wavelet shrinkage with Bayes rules and
Bayes factors.
By Brani Vidakovic, ISDS, Duke University.

Abstract. Wavelet shrinkage, the method proposed by
seminal work of Donoho and Johnstone is a disarmingly
simple and efficient way of de-noising data. Shrinking
wavelet coefficients was proposed from several
optimality criteria. The most notable are the
asymptotic minimax and cross-validation criteria.
In this paper a wavelet shrinkage by imposing
natural properties of Bayesian models on data
is proposed. The performance of methods are
tested on standard Donoho-Johnstone test functions.

It is on:
ftp isds.duke.edu //login: anonymous
~/pub/brani/papers/WavShrinkBF.ps.Z

Comments most welcome!
brani@isds.duke.edu
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