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

George Tzougas and Dimitris Karlis

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: mixed exponential distributions; EM algorithm; regression models for the mean and dispersion parameters; non-life insurance; heavy-tailed losses (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2020-05
New Economics Papers: this item is included in nep-ecm, nep-ias, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published in Astin Bulletin, May, 2020, 50(2), pp. 555 - 583. ISSN: 0515-0361

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:104027

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