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: |
Statistics and Probability
Generalized linear model explicit MLE heavy-tailed distributions 01 natural sciences insurance claim modeling 010104 statistics & probability [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] 0502 economics and business Statistics Statistics::Methodology 0101 mathematics Categorical variable 050205 econometrics Mathematics Sequence 05 social sciences Pareto principle Estimator Regression analysis Regression models Computational Mathematics Distribution (mathematics) Statistics Probability and Uncertainty Focus (optics) |
| 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 |
| Externí odkaz: |
|
Full text from SpringerLink