EM estimation for the bivariate mixed exponential regression model

Zezhun Chen, Angelos Dassios and George Tzougas

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: In this paper, we present a new family of bivariate mixed exponential regression models for taking into account the positive correlation between the cost of claims from motor third party liability bodily injury and property damage in a versatile manner. Furthermore, we demonstrate how maximum likelihood estimation of the model parameters can be achieved via a novel Expectation-Maximization algorithm. The implementation of two members of this family, namely the bivariate Pareto or, Exponential-Inverse Gamma, and bivariate Exponential-Inverse Gaussian regression models is illustrated by a real data application which involves fitting motor insurance data from a European motor insurance company.

Keywords: bivariate claim size modeling; regression models for the marginal means and dispersion parameters; motor third party liability insurance; expectation-maximization algorithm (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2022-05-17
New Economics Papers: this item is included in nep-ecm and nep-ias
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Published in Risks, 17, May, 2022, 10(5). ISSN: 2227-9091

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:115132

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