EM Estimation for the Bivariate Mixed Exponential Regression Model

Zezhun Chen, Angelos Dassios and George Tzougas
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Zezhun Chen: Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, UK
Angelos Dassios: Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, UK
George Tzougas: Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, UK

Risks, 2022, vol. 10, issue 5, 1-13

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: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:10:y:2022:i:5:p:105-:d:817586

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