Risk Bounds for Mixture Density Estimation

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 a bound on the estimation error which does not depend on the number of densities in the estimated combination.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA459846

Entities

People

  • Alexander Rakhlin
  • Dmitry Panchenko
  • Sayan Mukherjee

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Convergence
  • Errors
  • Inequalities
  • Information Operations
  • Instructions
  • Integrals
  • Massachusetts
  • Neural Networks
  • Probability
  • Probability Density Functions
  • Sequences
  • Standards
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Statistical inference.