Oracle Inequalities for Efromovich–Pinsker Blockwise Estimates

Sam Efromovich ()
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Sam Efromovich: The University of New Mexico

Methodology and Computing in Applied Probability, 2004, vol. 6, issue 3, 303-322

Abstract: Abstract Oracle inequality is a relatively new statistical tool for the analysis of nonparametric adaptive estimates. Oracle is a good pseudo-estimate that is based on both data and an underlying estimated curve. An oracle inequality shows how well an adaptive estimator mimics the oracle for a particular underlying curve. The most advanced oracle inequalities have been recently obtained by Cavalier and Tsybakov (2001) for Stein type blockwise estimates used in filtering a signal from a stationary white Gaussian process. The authors also conjecture that a similar result can be obtained for Efromovich–Pinsker (EP) type blockwise estimators where their approach, based on Stein's formula for risk calculation, does not work. This article proves the conjecture and extends it upon more general models which include not stationary and dependent processes. Other possible extensions, a discussion of practical implications and a numerical study are also presented.

Keywords: adaptation; asymptotic; filtering; mean integrated squared error; non-Gaussian noise; nonparametric; Stein estimator; wavelets (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1023/B:MCAP.0000026562.80429.48

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