Bivariate credibility bonus–malus premiums distinguishing between two types of claims
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DOI: 10.1016/j.insmatheco.2016.06.009
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References listed on IDEAS
- Dionne, Georges & Vanasse, Charles, 1989.
"A Generalization of Automobile Insurance Rating Models: The Negative Binomial Distribution with a Regression Component,"
ASTIN Bulletin, Cambridge University Press, vol. 19(2), pages 199-212, November.
- Dionne, G. & Vanasse, C., 1988. "A Generalization Of Automobile Insurance Rating Models: The Negative Binomial Distribution With A Regression Component," Cahiers de recherche 8833, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Dionne, G. & Vanasse, C., 1988. "A Generalization of Automobile Insurance Rating Models: the Negative Binomial Distribution with a Regression Component," Cahiers de recherche 8833, Universite de Montreal, Departement de sciences economiques.
- Heilmann, Wolf-Rudiger, 1989. "Decision theoretic foundations of credibility theory," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 77-95, March.
- de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, October.
- Sarabia, José María & Gómez-Déniz, Emilio & Vázquez-Polo, Francisco J., 2004. "On the Use of Conditional Specification Models in Claim Count Distributions: an Application to Bonus-Malus Systems," ASTIN Bulletin, Cambridge University Press, vol. 34(1), pages 85-98, May.
- Frangos, Nicholas E. & Vrontos, Spyridon D., 2001. "Design of Optimal Bonus-Malus Systems With a Frequency and a Severity Component On an Individual Basis in Automobile Insurance," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 1-22, May.
- Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
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Cited by:
- Simon, Pierre-Alexandre & Trufin, Julien & Denuit, Michel, 2023. "Bivariate Poisson credibility model and bonus-malus scale for claim and near-claim events," LIDAM Discussion Papers ISBA 2023014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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Keywords
Bayesian; Bonus–malus system; Claim; Claim size; Conjugate distribution; Relativity;All these keywords.
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