Bayesian Wavelet Shrinkage with Beta Priors
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We present a Bayesian approach for wavelet shrinkage in the context of non-parametric curve estimation with the use of the beta distribution with symmetric support around zero as the prior distribution for the location parameter in the wavelet domain in models with additive Gaussian errors. Explicit formulas of shrinkage rules for particular cases are obtained, statistical properties such as bias, classical and Bayesian risk of the rules are analyzed and performance of the proposed rules is assessed in simulations studies involving standard test functions. Application to Spike Sorting real data set is provided.
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