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Tzougas, George and Vrontos, Spyridon and Frangos, Nicholas (2014) 'OPTIMAL BONUS-MALUS SYSTEMS USING FINITE MIXTURE MODELS.' ASTIN Bulletin, 44 (2). pp. 417-444. ISSN 0515-0361

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<jats:title>Abstract</jats:title><jats:p>This paper presents the design of optimal Bonus-Malus Systems using finite mixture models, extending the work of Lemaire (1995; <jats:sc>Lemaire</jats:sc>, J. (1995) <jats:italic>Bonus-Malus Systems in Automobile Insurance</jats:italic>. Norwell, MA: Kluwer) and Frangos and Vrontos (2001; <jats:sc>Frangos</jats:sc>, N. and <jats:sc>Vrontos</jats:sc>, S. (2001) Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. <jats:italic>ASTIN Bulletin</jats:italic>, <jats:bold>31</jats:bold>(1), 1–22). Specifically, for the frequency component we employ finite Poisson, Delaporte and Negative Binomial mixtures, while for the severity component we employ finite Exponential, Gamma, Weibull and Generalized Beta Type II mixtures, updating the posterior probability. We also consider the case of a finite Negative Binomial mixture and a finite Pareto mixture updating the posterior mean. The generalized Bonus-Malus Systems we propose, integrate risk classification and experience rating by taking into account both the a priori and a posteriori characteristics of each policyholder.</jats:p>

Item Type: Article
Uncontrolled Keywords: Optimal BMS; claim frequency; claim severity; mixtures of distributions; a priori classification criteria; a posteriori classification criteria
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Nov 2014 10:40
Last Modified: 15 Jan 2022 00:47

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