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

Autor: Tom Rohmer, Christophe Dutang, Alexandre Brouste
Přispěvatelé: Laboratoire Manceau de Mathématiques (LMM), Le Mans Université (UM), CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), PANORisk
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Computational Statistics
Computational Statistics, Springer Verlag, In press, ⟨10.1007/s00180-019-00918-7⟩
ISSN: 0943-4062
1613-9658
DOI: 10.1007/s00180-019-00918-7⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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