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Risk Bounds for Mixture Density Estimation

Author(s)
Rakhlin, Alexander; Panchenko, Dmitry; Mukherjee, Sayan
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Abstract
In this paper we focus on the problem of estimating a boundeddensity using a finite combination of densities from a givenclass. 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.
Date issued
2004-01-27
URI
http://hdl.handle.net/1721.1/30443
Other identifiers
MIT-CSAIL-TR-2004-002
AIM-2004-001
CBCL-233
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI, density estimation, MLE

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