AN EM ALGORITHM FOR FITTING A NEW CLASS OF MIXED EXPONENTIAL REGRESSION MODELS WITH VARYING DISPERSION. Issue 2 (8th May 2020)

• Record Type: Journal Article
• Authors: Tzougas, George; Karlis, Dimitris
• Is Part Of: ASTIN bulletin. Volume 50:Issue 2(2020)
• Page Start: 555
• Page End: 583
• Publication Date: 2020-05-08
• Subjects: Mixed exponential distributions, -- EM algorithm, -- regression models for the mean and dispersion, -- parameters, -- non-life insurance, -- heavy-tailed losses; Insurance -- Mathematics -- Periodicals; Risk (Insurance) -- Mathematics -- Periodicals; 368.01
• DOI: 10.1017/asb.2020.13
• Languages: English
• ISSNs: 0515-0361

Abstract

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.