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August 2010 Asymptotic properties of maximum likelihood estimators in models with multiple change points
Heping He, Thomas A. Severini
Bernoulli 16(3): 759-779 (August 2010). DOI: 10.3150/09-BEJ232

Abstract

Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of this paper is to establish the asymptotic properties of maximum likelihood estimators of the parameters of a multiple change-point model for a general class of models in which the form of the distribution can change from segment to segment and in which, possibly, there are parameters that are common to all segments. Consistency of the maximum likelihood estimators of the change points is established and the rate of convergence is determined; the asymptotic distribution of the maximum likelihood estimators of the parameters of the within-segment distributions is also derived. Since the approach used in single change-point models is not easily extended to multiple change-point models, these results require the introduction of those tools for analyzing the likelihood function in a multiple change-point model.

Citation

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Heping He. Thomas A. Severini. "Asymptotic properties of maximum likelihood estimators in models with multiple change points." Bernoulli 16 (3) 759 - 779, August 2010. https://doi.org/10.3150/09-BEJ232

Information

Published: August 2010
First available in Project Euclid: 6 August 2010

zbMATH: 1220.62021
MathSciNet: MR2730647
Digital Object Identifier: 10.3150/09-BEJ232

Rights: Copyright © 2010 Bernoulli Society for Mathematical Statistics and Probability

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Vol.16 • No. 3 • August 2010
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