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

hal.structure.identifierLaboratoire Manceau de Mathématiques [LMM]
dc.contributor.authorBrouste, Alexandre
HAL ID: 14240
ORCID: 0000-0001-6719-7432		*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorDutang, Christophe
HAL ID: 9174
ORCID: 0000-0001-6732-1501		*
hal.structure.identifierLaboratoire Manceau de Mathématiques [LMM]
dc.contributor.authorRohmer, Tom
HAL ID: 745097		*
dc.date.accessioned2019-09-04T11:19:23Z
dc.date.available2019-09-04T11:19:23Z
dc.date.issued2019
dc.identifier.issn0943-4062
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19679
dc.language.isoenen
dc.subjectRegression models
dc.subjectHeavy-tailed distributions
dc.subjectExplicit MLE
dc.subjectInsurance claim modeling
dc.subject.ddc519en
dc.titleClosed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenGeneralized 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.
dc.relation.isversionofjnlnameComputational Statistics
dc.relation.isversionofjnlvol35
dc.relation.isversionofjnldate2019
dc.relation.isversionofjnlpages689–724
dc.relation.isversionofdoi10.1007/s00180-019-00918-7
dc.relation.isversionofjnlpublisherSpringer
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingouien
dc.relation.forthcomingprintouien
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2020-07-01T12:37:43Z
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