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

Tzougas, George and Karlis, Dimitris (2020) An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion. Astin Bulletin, 50 (2). 555 - 583. ISSN 0515-0361

[img] Text (An EM Algorithm for Fitting a New Class of Mixed Exponential Regression Models with Varying Dispersion) - Accepted Version
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Identification Number: 10.1017/asb.2020.13


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.

Item Type: Article
Official URL:
Additional Information: © 2020 Astin Bulletin
Divisions: Statistics
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Date Deposited: 07 Apr 2020 14:45
Last Modified: 20 Oct 2021 03:30

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