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On EM algorithms and their proximal generalizations
ESAIM: Probability and Statistics, Tome 12 (2008), pp. 308-326 Cet article a éte moissonné depuis la source Numdam

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In this paper, we analyze the celebrated EM algorithm from the point of view of proximal point algorithms. More precisely, we study a new type of generalization of the EM procedure introduced in [Chretien and Hero (1998)] and called Kullback-proximal algorithms. The proximal framework allows us to prove new results concerning the cluster points. An essential contribution is a detailed analysis of the case where some cluster points lie on the boundary of the parameter space.

DOI : 10.1051/ps:2007041
Classification : 65C20, 65C60
Keywords: maximum likelihood estimation (MLE), EM algorithm, proximal point algorithm, Karush-Kuhn-Tucker condition, mixture densities, competing risks models 
@article{PS_2008__12__308_0,
     author = {Chr\'etien, St\'ephane and Hero, Alfred O.},
     title = {On {EM} algorithms and their proximal generalizations},
     journal = {ESAIM: Probability and Statistics},
     pages = {308--326},
     year = {2008},
     publisher = {EDP-Sciences},
     volume = {12},
     doi = {10.1051/ps:2007041},
     mrnumber = {2404033},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/ps:2007041/}
}
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%J ESAIM: Probability and Statistics
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%I EDP-Sciences
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Chrétien, Stéphane; Hero, Alfred O. On EM algorithms and their proximal generalizations. ESAIM: Probability and Statistics, Tome 12 (2008), pp. 308-326. doi: 10.1051/ps:2007041

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