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An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion
Recurso electrónico / Electronic resource
MARC record
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001  MAP20200019121
003  MAP
005  20200610090110.0
008  200604e20200501bel|||p |0|||b|eng d
040  ‎$a‎​MAP‎$b‎​spa‎$d‎​MAP
084  ‎$a‎​6
1001 ‎$0‎​MAPA20140009800‎$a‎​Tzougas, George ‌
24510‎$a‎​An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion‎$c‎​George Tzougas, Dimitris Karlis
520  ‎$a‎​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.
650 4‎$0‎​MAPA20080579258‎$a‎​Cálculo actuarial ‌
650 4‎$0‎​MAPA20080553128‎$a‎​Algoritmos ‌
650 4‎$0‎​MAPA20080592011‎$a‎​Modelos actuariales ‌
650 4‎$0‎​MAPA20080602611‎$a‎​Modelos de dispersión ‌
7001 ‎$0‎​MAPA20200013471‎$a‎​Karlis, Dimitris ‌
7730 ‎$w‎​MAP20077000420‎$t‎​Astin bulletin‎$d‎​Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎​0515-0361‎$g‎​01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 555-583