On-line expectation-maximization algorithm for latent data models

We propose a generic on-line (also sometimes called adaptive or recursive) version of the expectation-maximization (EM) algorithm applicable to latent variable models of independent observations. Compared with the algorithm of Titterington, this approach is more directly connected to the usual EM al...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 71; H. 3; S. 593 - 613
Hauptverfasser:	Cappé, Olivier, Moulines, Eric
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.06.2009 Blackwell Publishing Ltd Blackwell Publishing Blackwell Royal Statistical Society Oxford University Press
Schriftenreihe:	Journal of the Royal Statistical Society Series B
Schlagworte:	Adaptive algorithms Algorithms Approximation Convergence Data Data models Distribution Distribution theory Exact sciences and technology Expectation-maximization General topics Internet Latent data models Linear regression Mathematical analysis Mathematical models Mathematics Matrices Maximum likelihood estimation Measure and integration Mixture of regressions On-line estimation Parametric inference Parametric models Polyak-Ruppert averaging Probabilities Probability and statistics probability distribution Probability theory and stochastic processes Recursion Regression analysis Sciences and techniques of general use Statistical methods Statistical models Statistics Stochastic approximation Stochastic models Studies Conditional distribution Divergence Adaptive algorithm Stochastic approximation Latent data models On-line estimation Linear model Convergence Hypothesis test Statistical test Sequential method Marginal distribution Distribution function Mixed distribution Integration Expectation―maximization Maximization Mixture of Linear regression Adaptive algorithms Latent variable model regressions Independent variable Statistical method Statistical regression Polyak―Ruppert averaging Regression model Maximum likelihood Expectation EM algorithm
ISSN:	1369-7412, 1467-9868
Online-Zugang:	Volltext
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