Convergence of the EM algorithm for continuous mixing distributions

Beyond the expectation–maximization (EM) algorithm for vector parameters, the EM for an unknown distribution function is often used in mixture models, density estimation, and signal recovery problems. We prove the convergence of the EM in functional spaces and show the EM likelihoods in this space converge to the global maximum.

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Year of publication:	2015
Authors:	Chung, Yeojin ; Lindsay, Bruce G.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 96.2015, C, p. 190-195
Publisher:	Elsevier
Subject:	EM algorithm \| Nonparametric maximum likelihoods \| Nonparametric mixture models \| Linear inverse problems

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011115951