
Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling
Brouste, Alexandre; Dutang, Christophe; Rohmer, Tom (2019), Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling, Computational Statistics, 35, p. 689–724. 10.1007/s00180-019-00918-7
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Type
Article accepté pour publication ou publiéDate
2019Journal name
Computational StatisticsVolume
35Publisher
Springer
Pages
689–724
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Show full item recordAuthor(s)
Brouste, Alexandre
Laboratoire Manceau de Mathématiques [LMM]
Dutang, Christophe

CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Rohmer, Tom
Laboratoire Manceau de Mathématiques [LMM]
Abstract (EN)
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.Subjects / Keywords
Regression models; Heavy-tailed distributions; Explicit MLE; Insurance claim modelingRelated items
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