Electronic Journal of Statistics

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

L. Montuelle and E. Le Pennec

Full-text: Open access

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.

Article information

Source
Electron. J. Statist. Volume 8, Number 1 (2014), 1661-1695.

Dates
First available in Project Euclid: 11 September 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1410441420

Digital Object Identifier
doi:10.1214/14-EJS939

Mathematical Reviews number (MathSciNet)
MR3263134

Zentralblatt MATH identifier
1297.62091

Subjects
Primary: 62G08: Nonparametric regression

Keywords
Mixture of Gaussian regressions models mixture of regressions models penalized likelihood model selection

Citation

Montuelle, L.; Le Pennec, E. Mixture of Gaussian regressions model with logistic weights, a penalized maximum likelihood approach. Electron. J. Statist. 8 (2014), no. 1, 1661--1695. doi:10.1214/14-EJS939. https://projecteuclid.org/euclid.ejs/1410441420


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