Sur la convergence de l'estimation conditionnelle itérative

Wojciech Pieczynski

doi:10.1016/j.crma.2008.02.023

Statistique

Sur la convergence de l'estimation conditionnelle itérative

Présenté par : Paul Deheuvels

Wojciech Pieczynski ¹

¹ Institut TELECOM ; TELECOM et Management SudParis ; Dépt. CITI ; CNRS UMR 5157, 9, rue Charles-Fourier, 91000 Evry, France

Comptes Rendus. Mathématique, Volume 346 (2008) no. 7-8, pp. 457-460.

Résumé
Abstract

L'estimation conditionnelle itérative (ECI) est une méthode d'estimation itérative des paramètres dans le cas des données incomplètes. Sa mise en œuvre demande des hypothèses relativement faibles et peut être effectuée dans des situations relativement complexes, comme les modèles de Markov triplets. L'objet de cette Note est d'énoncer un théorème général de convergence de l'ECI, et de montrer son applicabilité dans le problème de l'estimation des proportions dans un mélange de lois multi-variées.

The iterative conditional estimation (ICE) is an iterative estimation method of the parameters in the case of incomplete data. Its use asks for relatively weak hypotheses and it can be performed in relatively complex situations, as in triplet Markov models. The aim of this Note is to express a general theorem of convergence of ICE, and to show its applicability in the problem of the estimation of the proportions in a mixture of multivariate distributions.

Reçu le : 2007-04-05
Accepté le : 2008-02-25
Publié le : 2008-03-28

DOI : 10.1016/j.crma.2008.02.023

Affiliations des auteurs :

Wojciech Pieczynski ¹

¹ Institut TELECOM ; TELECOM et Management SudParis ; Dépt. CITI ; CNRS UMR 5157, 9, rue Charles-Fourier, 91000 Evry, France

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Wojciech Pieczynski. Sur la convergence de l'estimation conditionnelle itérative. Comptes Rendus. Mathématique, Volume 346 (2008) no. 7-8, pp. 457-460. doi : 10.1016/j.crma.2008.02.023. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2008.02.023/

Bibliographie
Cité par

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Wojciech Pieczynski Triplet Markov Chains and Image Segmentation, Inverse Problems in Vision and 3D Tomography (2013), p. 123 | DOI:10.1002/9781118603864.ch4
Florence Forbes; Wojciech Pieczynski, 2009 IEEE International Workshop on Machine Learning for Signal Processing (2009), p. 1 | DOI:10.1109/mlsp.2009.5306255

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