Empirical Bayesian Spatial Prediction Using Wavelets
نویسندگان
چکیده
Wavelet shrinkage methods, introduced by Donoho and John-, are a powerful way to carry out signal denoising, especially when the underlying signal has a sparse wavelet representation. Wavelet shrinkage based on the Bayesian approach involves specifying a prior distribution for the wavelet coeecients. In this chapter, we consider a Gaussian prior with nonzero means for wavelet coeecients, which is diierent from other priors used in the literature. An empirical Bayes approach is taken by estimating the mean parameters using Q-Q plots, and the hyperparameters of the prior covariance are estimated by a pseudo maximum likelihood method. A simulation study shows that our empirical Bayesian spatial prediction approach outperforms the well known VisuShrink and SureShrink methods for recovering a wide variety of signals.
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تاریخ انتشار 1999