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 k ernels however, it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone. We describe a

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... principle for spatially-adaptive estimation: selective wavelet reconstruction. W e s h o w t h a t v ariableknot spline ts and piecewise-polynomial ts, when equipped with an oracle to select the knots, are not dramatically more powerful than selective w avelet reconstruction with an oracle. We d e v elop a practical spatially adaptive method, RiskShrink, which w orks by shrinkage of empirical wavelet coe cients. RiskShrink mimics the performance of an oracle for selective w avelet reconstruction as well as it is possible to do so. A new inequality i n m ultivariate normal decision theory which w e c a l l t h e oracle inequality shows that attained performance di ers from ideal performance by at most a factor 2 l o g n, 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.