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EM estimation for the bivariate mixed exponential regression model

Chen, Zezhun, Dassios, Angelos and Tzougas, George (2022) EM estimation for the bivariate mixed exponential regression model. Risks, 10 (5). ISSN 2227-9091

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Identification Number: 10.3390/risks10050105

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.

Item Type: Article
Official URL: https://www.mdpi.com/journal/risks
Additional Information: © 2022 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 18 May 2022 08:48
Last Modified: 14 Jun 2022 09:12
URI: http://eprints.lse.ac.uk/id/eprint/115132

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