Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Σύστημα Bonus-Malus× | Ανάλυση Παλινδρόμησης Αρνητικού Διωνύμου× | |
|---|---|---|
| Πεδίο≠ | Αναλογιστική Επιστήμη | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1995 | 2011 |
| Δημιουργός≠ | Jean Lemaire | Hilbe (textbook treatment); generalized linear model framework |
| Τύπος≠ | Actuarial experience-rating model | Generalized linear model for count data |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | No-Claim Discount System, Merit Rating System, Experience Rating in Automobile Insurance, Prim-Ceza Sistemi | NB regression, NB2 regression, negatif binom regresyonu |
| Συναφείς≠ | 2 | 4 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|