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2014 Mixture of Gaussian regressions model with logistic weights, a penalized maximum likelihood approach
L. Montuelle, E. Le Pennec
Electron. J. Statist. 8(1): 1661-1695 (2014). DOI: 10.1214/14-EJS939

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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L. Montuelle. E. Le Pennec. "Mixture of Gaussian regressions model with logistic weights, a penalized maximum likelihood approach." Electron. J. Statist. 8 (1) 1661 - 1695, 2014. https://doi.org/10.1214/14-EJS939

Information

Published: 2014
First available in Project Euclid: 11 September 2014

zbMATH: 1297.62091
MathSciNet: MR3263134
Digital Object Identifier: 10.1214/14-EJS939

Subjects:
Primary: 62G08

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.8 • No. 1 • 2014
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