השוואת שיטות

	מערכת בונוס-מלוס	תורת האמינות	רגרסיית בינום שלילי
תחום	מדע אקטוארי	מדע אקטוארי	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	1995	1967	2011
הוגה השיטה	Jean Lemaire	Hans Bühlmann	Hilbe (textbook treatment); generalized linear model framework
סוג	Actuarial experience-rating model	Weighted linear blend of individual and collective experience	Generalized linear model for count data
מקור מכונן	Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers. ISBN: 978-0-7923-9545-5	Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin, 4(3), 199–207.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
כינויים	No-Claim Discount System, Merit Rating System, Experience Rating in Automobile Insurance, Prim-Ceza Sistemi	Bühlmann Credibility, Experience Rating, Linear Credibility Estimator, Güvenilirlik Teorisi	NB regression, NB2 regression, negatif binom regresyonu
קשורות	2	3	4
תקציר	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.	Credibility Theory is an actuarial framework for estimating the pure premium of an individual risk by blending its own observed loss experience with the collective (portfolio) mean. Introduced by Hans Bühlmann in 1967, the method derives the optimal linear combination—the credibility-weighted premium—that minimises mean squared error. It extends classical experience rating to a rigorous statistical footing rooted in Bayesian and linear estimation principles.	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.