Λ-neighborhood wavelet shrinkage

Norbert Reményi and Brani Vidakovic

Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 404-416

Abstract: We propose a wavelet-based denoising methodology based on total energy of a neighboring pair of coefficients plus their “parental” coefficient. The model is based on a Bayesian hierarchical model using a contaminated exponential prior on the total mean energy in a neighborhood of wavelet coefficients. The hyperparameters in the model are estimated by the empirical Bayes method, and the posterior mean, median and Bayes factor are obtained and used in the estimation of the total mean energy. Shrinkage of the neighboring coefficients are based on the ratio of the estimated and the observed energy. It is shown that the methodology is comparable and often superior to several existing and established wavelet denoising methods that utilize neighboring information, which is demonstrated by extensive simulations on a standard battery of test functions. An application to real-word data set from inductance plethysmography is also considered.

Keywords: Bayesian estimation; Block thresholding; Empirical Bayes method; Noncentral chi-square; Nonparametric regression (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:57:y:2013:i:1:p:404-416

DOI: 10.1016/j.csda.2012.07.008

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