Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling

Alexandre Brouste, Christophe Dutang () and Tom Rohmer
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Alexandre Brouste: Le Mans Université
Christophe Dutang: Univ. Paris-Dauphine, Univ. PSL
Tom Rohmer: Le Mans Université

Computational Statistics, 2020, vol. 35, issue 2, No 13, 689-724

Abstract: Abstract Generalized linear models with categorical explanatory variables are considered and parameters of the model are estimated by an exact maximum likelihood method. The existence of a sequence of maximum likelihood estimators is discussed and considerations on possible link functions are proposed. A focus is then given on two particular positive distributions: the Pareto 1 distribution and the shifted log-normal distributions. Finally, the approach is illustrated on an actuarial dataset to model insurance losses.

Keywords: Regression models; Heavy-tailed distributions; Explicit MLE; Insurance claim modeling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-019-00918-7

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