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Empirical Bayes Estimation in Wavelet Nonparametric Regression

  • Merlise A. Clyde
  • Edward I. George
Part of the Lecture Notes in Statistics book series (LNS, volume 141)

Abstract

Bayesian methods based on hierarchical mixture models have demonstrated excellent mean squared error properties in constructing data dependent shrinkage estimators in wavelets, however, subjective elicitation of the hyperparameters is challenging. In this chapter we use an Empirical Bayes approach to estimate the hyperparameters for each level of the wavelet decomposition, bypassing the usual difficulty of hyperparameter specification in the hierarchical model. The EB approach is computationally competitive with standard methods and offers improved MSE performance over several Bayes and classical estimators in a wide variety of examples.

Keywords

Discrete Wavelet Transformation Shrinkage Estimator Wavelet Shrinkage Posterior Model Probability Conditional Maximum Likelihood Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Merlise A. Clyde
  • Edward I. George

There are no affiliations available

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