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Estimating the parameters of a seasonal Markov-modulated Poisson process

Abstract : Motivated by seasonality and regime-switching features of some insurance claim counting processes, we study the statistical analysis of a Markov-modulated Poisson process featuring seasonality. We prove the strong consistency and the asymptotic normality of a maximum split-time likelihood estimator of the parameters of this model, and present an algorithm to compute it in practice. The method is illustrated on a small simulation study and a real data analysis.
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Contributor : Gilles Stupfler <>
Submitted on : Friday, February 24, 2017 - 11:05:52 AM
Last modification on : Friday, June 19, 2020 - 9:22:04 AM
Document(s) archivé(s) le : Thursday, May 25, 2017 - 12:20:48 PM


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  • HAL Id : hal-00965279, version 2


Armelle Guillou, Stéphane Loisel, Gilles Stupfler. Estimating the parameters of a seasonal Markov-modulated Poisson process. 2014. ⟨hal-00965279v2⟩



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