Gaussian Mixture Regression model with logistic weights, a penalized maximum likelihood approach

Lucie Montuelle 1, 2 Erwan Le Pennec 1, 2 Serge Cohen 3
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : We wish to estimate conditional density using Gaussian Mixture Regression model with logistic weights and means depending on the covariate. We aim at selecting the number of components of this model as well as the other parameters by a penalized maximum likelihood approach. We provide a lower bound on penalty, proportional up to a logarithmic term to the dimension of each model, that ensures an oracle inequality for our estimator. Our theoretical analysis is supported by some numerical experiments.
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Rapport
[Research Report] RR-8281, INRIA. 2013
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https://hal.inria.fr/hal-00809735
Contributeur : Erwan Le Pennec <>
Soumis le : mardi 9 avril 2013 - 17:07:57
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
Document(s) archivé(s) le : mercredi 10 juillet 2013 - 10:50:07

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RR-8281.pdf
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  • HAL Id : hal-00809735, version 1
  • ARXIV : 1304.2696

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Lucie Montuelle, Erwan Le Pennec, Serge Cohen. Gaussian Mixture Regression model with logistic weights, a penalized maximum likelihood approach. [Research Report] RR-8281, INRIA. 2013. 〈hal-00809735〉

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