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

Lucie Montuelle 1, 2 Erwan Le Pennec 3, 2
2 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 : In the framework of conditional density estimation, we use candidates taking the form of mixtures of Gaussian regressions with logistic weights and means depending on the covariate. We aim at estimating the number of components of this mixture, as well as the other parameters, by a penalized maximum likelihood approach. We provide a lower bound on the penalty that ensures an oracle inequality for our estimator. We perform some numerical experiments that support our theoretical analysis.
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Article dans une revue
Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8 (1), pp.35. 〈10.1214/14-EJS939〉
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https://hal.inria.fr/hal-01101483
Contributeur : Lucie Montuelle <>
Soumis le : jeudi 8 janvier 2015 - 17:08:04
Dernière modification le : jeudi 10 mai 2018 - 01:43:40

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Lucie Montuelle, Erwan Le Pennec. Mixture of Gaussian regressions model with logistic weights, a penalized maximum likelihood approach. Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8 (1), pp.35. 〈10.1214/14-EJS939〉. 〈hal-01101483〉

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