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

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      <subfield code="a">An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion</subfield>
      <subfield code="c">George Tzougas, Dimitris Karlis</subfield>
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      <subfield code="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.</subfield>
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      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
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      <subfield code="g">01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 555-583</subfield>
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