Flexible empirical Bayes estimation for wavelets

Published

Journal Article

Wavelet shrinkage estimation is an increasingly popular method for signal denoising and compression. Although Bayes estimators can provide excellent mean-squared error (MSE) properties, the selection of an effective prior is a difficult task. To address this problem, we propose empirical Bayes (EB) prior selection methods for various error distributions including the normal and the heavier-tailed Student t-distributions. Under such EB prior distributions, we obtain threshold shrinkage estimators based on model selection, and multiple-shrinkage estimators based on model averaging. These EB estimators are seen to be computationally competitive with standard classical thresholding methods, and to be robust to outliers in both the data and wavelet domains. Simulated and real examples are used to illustrate the flexibility and improved MSE performance of these methods in a wide variety of settings.

Full Text

Duke Authors

Cited Authors

  • Clyde, M; George, EI

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 62 / 4

Start / End Page

  • 681 - 698

International Standard Serial Number (ISSN)

  • 1369-7412

Digital Object Identifier (DOI)

  • 10.1111/1467-9868.00257

Citation Source

  • Scopus