Wavelet Shrinkage with Double Weibull Prior

作者：Remenyi Norbert^*; Vidakovic Brani

来源：Communications in Statistics - Simulation and Computation, 2015, 44(1): 88-104.

DOI：10.1080/03610918.2013.765470

• Bayes estimation
• Double Weibull distribution
• Larger posterior mode
• Nonparametric regression
• Wavelet shrinkage

In this article, we propose a denoising methodology in the wavelet domain based on a Bayesian hierarchical model using Double Weibull prior. We propose two estimators, one based on posterior mean (Double Weibull Wavelet Shrinker, DWWS) and the other based on larger posterior mode (DWWS-LPM), and show how to calculate them efficiently. Traditionally, mixture priors have been used for modeling sparse wavelet coefficients. The interesting feature of this article is the use of non-mixture prior. We show that the methodology provides good denoising performance, comparable even to state-of-the-art methods that use mixture priors and empirical Bayes setting of hyper-parameters, which is demonstrated by extensive simulations on standardly used test functions. An application to real-word dataset is also considered.

• 出版日期2015