Closed form Maximum Likelihood Estimator for Generalized Linear Models in the case of categorical explanatory variables: Application to insurance loss modelling - Archive ouverte HAL Access content directly
Journal Articles Computational Statistics Year : 2020

Closed form Maximum Likelihood Estimator for Generalized Linear Models in the case of categorical explanatory variables: Application to insurance loss modelling

Abstract

Generalized Linear Models with categorical explanatory variables are considered and parameters of the model are estimated with an original 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 a actuarial dataset to model insurance losses.
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Dates and versions

hal-01781504 , version 1 (30-04-2018)
hal-01781504 , version 2 (05-05-2019)
hal-01781504 , version 3 (25-08-2019)

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Alexandre Brouste, Christophe Dutang, Tom Rohmer. Closed form Maximum Likelihood Estimator for Generalized Linear Models in the case of categorical explanatory variables: Application to insurance loss modelling. Computational Statistics, 2020, ⟨10.1007/s00180-019-00918-7⟩. ⟨hal-01781504v3⟩
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