Garrido, Jose and Zhou, Jun (2006) Credibililty Theory for Generalized Linear and Mixed Models. Technical Report. Concordia University. Department of Mathematics & Statistics, Montreal, Quebec.
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Abstract
Generalized linear models (GLMs) are gaining popularity as a statistical analysis method for insurance data. For segmented portfolios, as in car insurance, the question of credibility arises naturally; how many observations are needed in a risk class before the GLM estimators can be considered credible? In this paper we study the limited fluctuations credibility of the GLM estimators as well as in the extended case of generalized linear mixed model (GLMMs). We show how credibility depends on the sample size, the distribution of covariates and the link function. This provides a mechanism to obtain confidence intervals for the GLM and GLMM estimators.
Divisions: | Concordia University > Faculty of Arts and Science > Mathematics and Statistics |
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Item Type: | Monograph (Technical Report) |
Authors: | Garrido, Jose and Zhou, Jun |
Series Name: | Department of Mathematics & Statistics. Technical Report No. 5/06 |
Corporate Authors: | Concordia University. Department of Mathematics & Statistics |
Institution: | Concordia University |
Date: | December 2006 |
Keywords: | GLMs, GLMMs, limited fluctuations credibility, confidence intervals |
ID Code: | 6676 |
Deposited By: | DIANE MICHAUD |
Deposited On: | 03 Jun 2010 20:49 |
Last Modified: | 18 Jan 2018 17:29 |
References:
Aitkin, M., D. Anderson, B. Francis and J. Hinde, 1989. Statistical Modelling in GLIM. Oxford Science Publications, Oxford.Antonio, K. and J. Beirlant, 2006. Actuarial statistics with generalized linear mixed models. Insurance: Mathematics and Economics, in press.
Bühlmann, H., 1967. Experience rating and credibility I. ASTIN Bulletin 4(3), 199–207.
Bühlmann, H., 1969. Experience rating and credibility II. ASTIN Bulletin 5(2), 157–165 .
Bühlmann, H. and E. Straub, 1970. Glaubw¨urdigkeit f¨ur Schadensätze. Bulletin of the Swiss Association of Actuaries, 111–133.
Cordeiro, G.M. and P. McCullagh, 1991. Bias correction in generalized linear models. Journal of the Royal Statistical Society, B 53(3), 629–643.
Demindenko, E., 2004. Mixed Models: Theory and Applications. Hoboken, New Jersey.
Dobson, A. 1990. An Introduction to Generalized Linear Models. Chapman and Hall, London.
Frees, E.W., 2003. Multivariate credibility for aggregate loss models. North American Actuarial Journal 7, 13–37.
Goulet, V., A. Forgues and J. Lu, 2006. Credibility for severity revisited. North American Actuarial Journal 10, 49–62.
Haberman, S. and A.E. Renshaw, 1996. Generalized linear models and actuarial science. The Statistician 45(4), 407–436.
Hachemeister, C.A., 1975. Credibility for regression models with application to trend. Credibility, Theory and Application. Academic Press, New York, 129–163.
Jewell, W.S., 1975. The use of collateral data in credibility theory: A hierarchical model. Giornale dell’ Instituto Italiano degli Attuari 38, 1–16.
McCullagh, P. and J.A. Nelder, 1989. Generalized Linear Models. Chapman and Hall, New York.
McCulloch, C.E. and S.R. Searle, 2001. Generalized, Linear and MixedModels. Wiley, New York.
Nelder, J.A. and R.J. Verrall, 1997. Credibility theory and generalized linear models. ASTIN Bulletin 27(1), 71–82.
SAS Technical Report P–243, 1993. SAS/STAT Software: The GENMOD Procedure, Release 6.09, SAS Institute Inc., Cary, NC.
Schmitter, H. 2004. The sample size needed for the calculation of a GLM tariff. ASTIN Bulletin 34(1), 249–262.
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