## Abstract Bayesian waveletβshrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation (DWT) has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes
Cross-validated wavelet shrinkage
β Scribed by Hee-Seok Oh; Donghoh Kim; Youngjo Lee
- Publisher
- Springer
- Year
- 2008
- Tongue
- English
- Weight
- 798 KB
- Volume
- 24
- Category
- Article
- ISSN
- 0943-4062
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π SIMILAR VOLUMES
The results of Hall et al. (1998, Ann. Statist. 26, 922-943) together with Efromovich (2000, Bernoulli) imply that a data-driven block shrinkage wavelet estimator, which mimics a sharp minimax linear oracle, is rate optimal over spatially inhomogeneous function spaces. This result does not contradic
Wavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wavelet coefficients. Such simple estimators are now well explored and widely used in wavelet-based nonparametrics. Results of Tao (1996, Appl. Comput. Harmon. Anal. 3, 384-387) demonstrated that hard and soft th