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A DATA-DRIVEN BLOCK THRESHOLDING APPROACH TO WAVELET ESTIMATION
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DOI：10.1214/07-AOS538.png)
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Abstract
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A data-driven block thresholding procedure for wavelet regression is proposed and its theoretical and numerical properties are investigated. The procedure empirically chooses the block size and threshold level at each resolution level by minimizing Stein's unbiased risk estimate. The estimator is sharp adaptive over a class of Besov bodies and achieves simultaneously within a small constant factor of the minimax risk over a wide collection of Besov Bodies including both the dense and sparse cases. The procedure is easy to implement. Numerical results show that it has, superior finite sample performance in comparison to the other leading wavelet thresholding estimators.
Keywords:
Adaptivity
Besov body
block thresholding
James-Stein estimator
non-parametric regression
Stein's unbiased risk estimate
wavelets
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