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

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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https://hal.inria.fr/hal-00809735
Contributor : Erwan Le Pennec <>
Submitted on : Tuesday, April 9, 2013 - 5:07:57 PM
Last modification on : Tuesday, July 6, 2021 - 3:40:01 AM
Long-term archiving on: : Wednesday, July 10, 2013 - 10:50:07 AM

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  • HAL Id : hal-00809735, version 1
  • ARXIV : 1304.2696

Citation

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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