Bonus-Malus Systems with Two-Component Mixture Models Arising from Different Parametric Families

DOI10.1080/10920277.2017.1368398zbMath1393.62048OpenAlexW2726402734MaRDI QIDQ4567960

G. Tzougas, Nikolaos E. Frangos, Spyridon D. Vrontos

Publication date: 20 June 2018

Published in: North American Actuarial Journal (Search for Journal in Brave)

Full work available at URL: http://eprints.lse.ac.uk/84301/7/Tzougas_Vrontos_Frangos_Bonus%20Malus%20Systems.pdf

zbMATH Keywords

variance principleparametric familybonus-malus systeminsurance companies

Mathematics Subject Classification ID

Related Items (7)

The multivariate mixed negative binomial regression model with an application to insurance a posteriori ratemaking ⋮ Bivariate Mixed Poisson Regression Models with Varying Dispersion ⋮ Mixture Composite Regression Models with Multi-type Feature Selection ⋮ Frequency-severity experience rating based on latent Markovian risk profiles ⋮ AN EM ALGORITHM FOR FITTING A NEW CLASS OF MIXED EXPONENTIAL REGRESSION MODELS WITH VARYING DISPERSION ⋮ PREDICTIVE CLAIM SCORES FOR DYNAMIC MULTI-PRODUCT RISK CLASSIFICATION IN INSURANCE ⋮ The negative binomial-inverse Gaussian regression model with an application to insurance ratemaking

Uses Software

• GAMLSS

Cites Work

• Unnamed Item
• Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape
• Decision theoretic foundations of credibility theory
• Measuring sensitivity in a bonus-malus system.
• The Design of an Optimal Bonus-Malus System Based on the Sichel Distribution
• Actuarial Modelling of Claim Counts
• Mean and dispersion modelling for policy claims costs
• Designing Optimal Bonus-Malus Systems from Different Types of Claims
• Design of Optimal Bonus-Malus Systems With a Frequency and a Severity Component On an Individual Basis in Automobile Insurance
• Risk Classification for Claim Counts
• OPTIMAL BONUS-MALUS SYSTEMS USING FINITE MIXTURE MODELS
• Univariate Discrete Distributions
• Generalized Additive Models for Location, Scale and Shape

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