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Sharp adaptive estimation for d-dimensional classes

Abstract : In this paper, our aim is to estimate an anisotropic function in the framework of the Gaussian white noise model. We suppose that the function belongs to an unknown d-dimensional Holder class. We evaluate the performance of an estimator by the sup-norm risk. We find the exact asymptotics of the adaptive minimax risk and construct asymptotically adaptive estimators.
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https://hal.archives-ouvertes.fr/hal-00160734
Contributor : Karine Bertin <>
Submitted on : Friday, July 6, 2007 - 10:14:32 PM
Last modification on : Wednesday, October 16, 2019 - 2:48:40 PM
Long-term archiving on: : Monday, September 24, 2012 - 10:56:45 AM

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Karine Bertin. Sharp adaptive estimation for d-dimensional classes. 2005. ⟨hal-00160734⟩

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