Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7281
Title: Risk Bounds for Mixture Density Estimation
Keywords: AI
density estimation
MLE
Issue Date: 9-Oct-2013
Description: 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.
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: AIM-2004-001
CBCL-233
http://hdl.handle.net/1721.1/7281
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