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Risk Bounds for Mixture Density Estimation

In this paper we focus on the problem of estimating a bounded density using a finite combination of densities from a given class. We consider the Maximum Likelihood Procedure (MLE) and the greedy procedure described by Li and Barron. Approximation and estimation bounds are given for the above methods. We extend and improve upon the estimation results of Li and Barron, and in particular prove an $O(\\frac{1}{\\sqrt{n}})$ bound on the estimation error which does not depend on the number of densities in the estimated combination.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7281
Date27 January 2004
CreatorsRakhlin, Alexander, Panchenko, Dmitry, Mukherjee, Sayan
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format11 p., 1656004 bytes, 658609 bytes, application/postscript, application/pdf
RelationAIM-2004-001, CBCL-233

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