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

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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https://hal.inria.fr/hal-01101483
Contributor : Lucie Montuelle <>
Submitted on : Thursday, January 8, 2015 - 5:08:04 PM
Last modification on : Wednesday, September 16, 2020 - 5:09:18 PM

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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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