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- W296693017 abstract "The main purpose of this article is to study the wavelet shrinkage method from a Bayesian viewpoint. Nonparametric mixed-effects models are proposed and used for interpretation of the Bayesian structure. Bayes and empirical Bayes estima- tion are discussed. The latter is shown to have the Gauss-Markov type optimality (i.e., BLUP), to be equivalent to a method of regularization estimator (MORE), and to be minimax in a certain class. Characterization of prior and posterior reg- ularity is discussed. The smoothness of posterior estimators is controlled via prior parameters. Computational issues including the use of generalized cross validation are discussed, and examples are presented." @default.
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- W296693017 modified "2023-09-23" @default.
- W296693017 title "BAYESIAN WAVELET SHRINKAGE FOR NONPARAMETRIC MIXED-EFFECTS MODELS" @default.
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