Bayesian wavelet shrinkage
Version of Record online: 24 SEP 2010
© 2010 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 2, Issue 6, pages 668–672, November/December 2010
How to Cite
Huerta, G. (2010), Bayesian wavelet shrinkage. WIREs Comp Stat, 2: 668–672. doi: 10.1002/wics.127
- Issue online: 22 NOV 2010
- Version of Record online: 24 SEP 2010
- Bayes shrinkage;
- data denoising;
- discrete wavelet transformation;
- smooth shrinkage;
- multivariate Bayes shrinkage
Bayesian wavelet-shrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation (DWT) has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes shrinkage is performed, an Inverse DWT can be used to recover the signal that generated the observations. This article reviews some of the main approaches for Bayesian wavelet shrinkage that span both smooth and multivariate types of shrinkage. WIREs Comp Stat 2010 2 668–672 DOI: 10.1002/wics.127
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