An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion

George Tzougas*, Dimitris Karlis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (SciVal)

Abstract

Regression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM)-type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM-type algorithm.

Original languageEnglish
Pages (from-to)555-583
Number of pages29
JournalASTIN Bulletin
Volume50
Issue number2
Early online date8 May 2020
DOIs
Publication statusPublished - May 2020

Keywords

  • EM algorithm
  • heavy-tailed losses
  • Mixed exponential distributions
  • non-life insurance
  • parameters
  • regression models for the mean and dispersion

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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