Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-27T21:30:24.778Z Has data issue: false hasContentIssue false
  • English
  • Français

Premium Rating by Geographic Area Using Spatial Models

Published online by Cambridge University Press:  29 August 2014

M. Boskov  and
R. J. Verrall
M. Boskov*
Affiliation:
Department of Actuarial Science and Statistics, The City University
R. J. Verrall
Affiliation:
Department of Actuarial Science and Statistics, The City University
*
Department of Actuarial Science and Statistics, The City University, Northampton Square, London EC1V 0HB, England.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper gives a method for premium rating by postcode area. The method is based on spatial models in a Bayesian framework and uses the Gibbs sampler for estimation. A summary of the theory of Bayesian spatial methods is given and the data which was analysed by Taylor (1989) is reanalysed. An indication is given of the wide range of models within this class which would be suitable for insurance data. The aim of the paper is to introduce the models and to show how they can be utilised in an insurance setting.

Keywords

Gibbs samplerPostcodesPremium ratingSpatial statistics
Type
Workshop
Information
ASTIN Bulletin: The Journal of the IAA , Volume 24 , Issue 1 , May 1994 , pp. 131 - 143
Copyright
Copyright © International Actuarial Association 1994

References

REFERENCES

Besag, J. (1974) Spatial Interaction and the Statistical Analysis of Lattice Systems (with discussion). J. Royal Statist. Soc. Series B, Vol. 36, No. 2.Google Scholar
Besag, J., York, J. and Mollié, A. (1991) Bayesian Image Restoration, with Applications in Spatial Statistics (with discussion). Ann. Inst. Statist. Math., Vol. 43, 1–59.CrossRefGoogle Scholar
Cressie, N. (1991) Statistics for Spatial Data. John Wiley and Sons, New York.Google Scholar
Geman, S. and Geman, D. (1984) Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. I.E.E.E. Trans. Pattn Anal. Mach. Intell., Vol. 6, 721–741.Google ScholarPubMed
Gilks, W. R., Clayton, D.G., Spiegelhalter, D.J., Best, N.G.McNeil, A.J., Sharples, L. D. and Kirby, A.J. (1993) Modelling Complexity: Applications of Gibbs Sampling in Medicine. J. Royal Statist. Soc., Series B, Vol. 55, No. 1.Google Scholar
Gilks, W. R. and Wild, P. (1992) Adaptive Rejection Sampling for Gibbs Sampling. Appl. Statist., Vol. 41, 337–348.CrossRefGoogle Scholar
Renshaw, A. E. (1993) Modelling the Claims Process in the Presence of Covariates. ASTIN Colloquium.Google Scholar
Smith, A. F. M. and Roberts, G.O. (1993) Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods. J. Royal Statist. Soc., Series B, Vol. 55, No. 1.Google Scholar
Taylor, G.C. (1989) Use of Spline Functions for Premium Rating by Geographic Area. ASTIN Bulletin Vol. 19, No. 1, 91–122.CrossRefGoogle Scholar