Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Système Bonus-Malus× | Régression binomiale négative× | |
|---|---|---|
| Domaine≠ | Actuariat | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1995 | 2011 |
| Auteur d'origine≠ | Jean Lemaire | Hilbe (textbook treatment); generalized linear model framework |
| Type≠ | Actuarial experience-rating model | Generalized linear model for count data |
| Source fondatrice≠ | Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers. ISBN: 978-0-7923-9545-5 | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Alias≠ | No-Claim Discount System, Merit Rating System, Experience Rating in Automobile Insurance, Prim-Ceza Sistemi | NB regression, NB2 regression, negatif binom regresyonu |
| Apparentées≠ | 2 | 4 |
| Résumé≠ | A Bonus-Malus System (BMS) is an actuarial experience-rating mechanism used primarily in automobile insurance to adjust individual policyholders' premiums based on their personal claim history. Policyholders who remain claim-free receive premium discounts (bonus), while those who file claims are penalised with surcharges (malus). The framework was comprehensively formalised and analysed by Jean Lemaire in his landmark 1995 monograph, which remains the definitive reference for the design and evaluation of such systems worldwide. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
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