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Mixing Strategies for Density Estimation
The Annals of Statistics
Vol. 28, No. 1 (Feb., 2000), pp. 75-87
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2673982
Page Count: 13
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General results on adaptive density estimation are obtained with respect to any countable collection of estimation strategies under Kullback-Leibler and squared L2 losses. It is shown that without knowing which strategy works best for the underlying density, a single strategy can be constructed by mixing the proposed ones to be adaptive in terms of statistical risks. A consequence is that under some mild conditions, an asymptotically minimax-rate adaptive estimator exists for a given countable collection of density classes; that is, a single estimator can be constructed to be simultaneously minimax-rate optimal for all the function classes being considered. A demonstration is given for high-dimensional density estimation on [0, 1]d where the constructed estimator adapts to smoothness and interaction-order over some piecewise Besov classes and is consistent for all the densities with finite entropy.
The Annals of Statistics © 2000 Institute of Mathematical Statistics