EM Estimation for the Bivariate Mixed Exponential Regression Model

Zezhun Chen, Angelos Dassios, George Tzougas

Research output: Contribution to journalArticlepeer-review

43 Downloads (Pure)

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.
Original languageEnglish
Article number105
JournalRisks
Volume10
Issue number5
DOIs
Publication statusPublished - 17 May 2022

Keywords

  • Expectation-Maximization algorithm
  • bivariate claim size modeling
  • motor third party liability insurance
  • regression models for the marginal means and dispersion parameters

ASJC Scopus subject areas

  • Accounting
  • Economics, Econometrics and Finance (miscellaneous)
  • Strategy and Management

Fingerprint

Dive into the research topics of 'EM Estimation for the Bivariate Mixed Exponential Regression Model'. Together they form a unique fingerprint.

Cite this