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Ideal spatial adaptation by wavelet shrinkage (1994)
| Venue: | Biometrika |
| Citations: | 1265 - 5 self |
BibTeX
@ARTICLE{Donoho94idealspatial,
author = {David L. Donoho and Iain M. Johnstone},
title = {Ideal spatial adaptation by wavelet shrinkage},
journal = {Biometrika},
year = {1994},
volume = {81},
pages = {425--455}
}
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Abstract
With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline, or variable bandwidth kernel, to the unknown function. Estimation with the aid of an oracle o ers dramatic advantages over traditional linear estimation by nonadaptive kernels � however, it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone. We describe a new principle for spatially-adaptive estimation: selective wavelet reconstruction. Weshowthatvariableknot spline ts and piecewise-polynomial ts, when equipped with an oracle to select the knots, are not dramatically more powerful than selective wavelet reconstruction with an oracle. We develop a practical spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coe cients. RiskShrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so. A new inequality inmultivariate normal decision theory which wecallthe oracle inequality shows that attained performance di ers from ideal performance by at most a factor 2logn, where n is the sample size. Moreover no estimator can give a better guarantee than this. Within the class of spatially adaptive procedures, RiskShrink is essentially optimal. Relying only on the data, it comes within a factor log 2 n of the performance of piecewise polynomial and variable-knot spline methods equipped with an oracle. In contrast, it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone.
Keyphrases
ideal spatial adaptation selective wavelet reconstruction wavelet shrinkage sample size variable bandwidth kernel oracle inequality empirical wavelet coe cients variable knot spline piecewise constant adaptive method piecewise polynomial traditional linear estimation variable-knot spline method nonadaptive kernel factor log weshowthatvariableknot spline t ideal performance variable estimator piecewise-polynomial t oracle er dramatic advantage performance di er spatially-adaptive estimation unknown function new principle piecewise polynomial method adaptive procedure
