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2017 Model selection for the segmentation of multiparameter exponential family distributions
Alice Cleynen, Emilie Lebarbier
Electron. J. Statist. 11(1): 800-842 (2017). DOI: 10.1214/17-EJS1246

Abstract

We consider the segmentation problem of univariate distributions from the exponential family with multiple parameters. In segmentation, the choice of the number of segments remains a difficult issue due to the discrete nature of the change-points. In this general exponential family distribution framework, we propose a penalized $\log$-likelihood estimator where the penalty is inspired by papers of L. Birgé and P. Massart. The resulting estimator is proved to satisfy some oracle inequalities. We then further study the particular case of categorical variables by comparing the values of the key constants when derived from the specification of our general approach and when obtained by working directly with the characteristics of this distribution. Finally, simulation studies are conducted to assess the performance of our criterion and to compare our approach to other existing methods, and an application on real data modeled using the categorical distribution is provided.

Citation

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Alice Cleynen. Emilie Lebarbier. "Model selection for the segmentation of multiparameter exponential family distributions." Electron. J. Statist. 11 (1) 800 - 842, 2017. https://doi.org/10.1214/17-EJS1246

Information

Received: 1 July 2016; Published: 2017
First available in Project Euclid: 28 March 2017

zbMATH: 1362.62068
MathSciNet: MR3629016
Digital Object Identifier: 10.1214/17-EJS1246

Subjects:
Primary: 62G05, 62G07
Secondary: 62P10

JOURNAL ARTICLE
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Vol.11 • No. 1 • 2017
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