Risk Bounds for Mixture Density Estimation
Author(s) • •
Rakhlin, Alexander
Panchenko, Dmitry
Mukherjee, Sayan
Date Issued
January 27, 2004
Series/Report no.
AIM-2004-001
CBCL-233
Abstract
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.
Subjects
AI
density estimation
MLE
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