Statistical learning algorithms applied to automobile insurance ratemaking

Intelligent And Other Computational Techniques In Insurance: Theory And Applications

Volumn , Issue , 2003, Pages 137-197

  Dugas, Charles ^a   Bengio, Yoshua ^a   Chapados, Nicolas ^a   Vincent, Pascal ^a   Denoncourt, Germain ^b   Fournier, Christian ^c  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b Alpha Compagnie d Assurances Inc   (Canada)

^c Insurance Corporation of British Columbia   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85116532934     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/9789812794246_0004     Document Type: Chapter

References (49)

1. Aldrich, J. 1995, R.A. Fisher and the making of maximum likelihood 1912-22, Technical Report 9504, University of Southampton, Department of Economics.
2. Bailey, R. and Simon, L. 1960, ‘Two studies in automobile insurance ratemaking’, ASTIN Bulletin 1(4), 192-217.
3. Bellman, R. 1957, Dynamic Programming, Princeton University Press, NJ.
4. Bengio, Y. and Gingras, F. 1996, Recurrent neural networks for missing or asynchronous data, in M. Mozer, D. Touretzky and M. Perrone, eds, ‘Advances in Neural Information Processing System’, Vol. 8, MIT Press, Cambridge, MA, pp. 395-401.
5. Bishop, C. 1995, Neural Networks for Pattern Recognition, Oxford University Press.
6. Breiman, L., Friedman, J., Olshen, R. and Stone, C. 1984, Classification and Regression Trees, Wadsworth Int. Group.
7. Brown, R. 1988, Minimum bias with generalized linear models, in ‘Proceedings of the Casualty Actuarial Society’.
8. Campbell, J., Lo, A. W. and MacKinlay, A. 1997, The Econometrics of Financial Markets, Princeton University Press, Princeton.
9. Cantelli, F. 1933, ‘Sulla probabilità come limita della frequenza’, Rend. Accad. Lincei 26(1), 39.
10. Chapados, N. and Bengio, Y. 2003, ‘Extensions to metric-based model selection’, Journal of Machine Learning Research 3, 1209-1227. Special Issue on Feature Selection.
11. Cristianini, N. and Shawe-Taylor, J. 2000, An Introduction to Support Vector Machines, Cambridge Press.
12. Dempster, A. P., Laird, N. M. and Rubin, D. B. 1977, ‘Maximum-likelihood from incomplete data via the EM algorithm’, Journal of Royal Statistical Society B 39, 1-38.
13. Diebold, F. X. and Mariano, R. S. 1995, ‘Comparing predictive accuracy’, Journal of Business and Economic Statistics 13(3), 253-263.
14. Dugas, C., Bengio, Y., Bélisle, F., Nadeau, C. and Garcia, R. 2001, A universal approximator of convex functions applied to option pricing, in ‘Advances in Neural Information Processing Systems’, Vol. 13, Denver, CO.
15. Fisher, R. A. 1912, ‘On an absolute citerion for frequency curves’, Messenger of Mathematics 41, 155-160.
16. Fisher, R. A. 1915, ‘Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population’, Biometrika 10, 507-521.
17. Fisher, R. A. 1922, ‘On the mathematical foundations of theoretical statistics’, Philosophical Transactions of the Royal Society of London A222, 309-368.
18. Fisher, R. A. 1925, ‘Theory of statistical estimation’, Proceedings of the Cambridge Philosophical Society 22, 700-725.
19. Geman, S., Bienenstock, E. and Doursat, R. 1992, ‘Neural networks and the bias/variance dilemma’, Neural Computation 4(1), 1-58.
20. Ghahramani, Z. and Jordan, M. I. 1994, Supervised learning from incomplete data via an EM approach, in J. Cowan, G. Tesauro and J. Alspector, eds, ‘Advances in Neural Information Processing Systems’, Vol. 6, Morgan Kaufmann, San Mateo, CA.
21. Gingras, F., Bengio, Y. and Nadeau, C. 2000, On out-of-sample statistics for time-series, in ‘Computational Finance 2000'.
22. Glivenko, V. 1933, ‘Sulla determinazione empirica delle leggi di probabilità', Giornale dell’Istituta Italiano degli Attuari 4, 92.
23. Hampel, R, Ronchetti, E., Rousseeuw, R and Stahel, W. 1986, Robust Statistics, The Approach based on Influence Functions, John Wiley & Sons.
24. Hastie, T., Tibshirani, R. and Friedman, J. 2001, Data Mining, Inference and Prediction, Springer.
25. Holler, K., Sommer, D. and Trahair, G. 1999, ‘Something old, something new in classification ratemaking with a novel use of glms for credit insurance’, Casualty Actuarial Society Forum pp. 31-84.
26. Huber, R. 1982, Robust Statistics, John Wiley & Sons Inc.
27. Huber, R.J. 1964, ‘Robust estimation of a location parameter’, Annals of Mathematical Statistics 35, 73-101.
28. Huber, R.J. 1965, ‘A robust version of the probability ratio’, Annals of Mathematical Statistics 36, 17-53.
29. Huber, P.J. 1966, ‘Strict efficiency excludes superefficiency’, Annals of Mathematical Statistics 37, 14-25.
30. Huber, P.J. 1968, ‘Robust confidence limits’, Zeitschrift fur Wahrscheinlichkeits Theorie 10, 269-278.
31. Huber, P.J. 1981, Robust Statistics, John Wiley & Sons, New York.
32. Huber, P.J. 1985, ‘Projection pursuit’, Annals of Statistics 13, 435-525.
33. Huber, P. J. and Rieder, H. 1996, Robust statistics, data analysis, and computer intensive methods, number 109 in ‘Lecture Notes in Statistics’, Springer, New York.
34. Jacobs, R. A., Jordan, M. I., Nowlan, S. J. and Hinton, G. E. 1991, ‘Adaptive mixture of local experts’, Neural Computation 3, 79-87.
35. Kass, G. 1980, ‘An exploratory technique for investigating large quantities of categorical data’, Applied Statistics 29(2), 119-127.
36. Keller, P.E. 1997, ‘Neural networks: Commercial applications’. http://www.emsl.pnl.gov.2080/proj/neuron/neural/products.
37. Kolmogorov, A. 1933, ‘Sulla determinazione empirica di una leggi di distribuzione’, Giornale dell’Istituta Italiano degli Attuari 4, 33.
38. McCullagh, P. and Nelder, J. 1989, Generalized Linear Models, Chapman and Hall, London.
39. Murphy, K., Brockman, M. and Lee, P. 2000, ‘Using generalized linear models to build dynamic pricing systems’, Casualty Actuarial Society Forum pp. 107-139.
40. Nadeau, C. and Bengio, Y. 2000, Inference for the generalization error, in S. Solla, T. Leen and K.-R. Mller, eds, ‘Advances in Neural Information Processing Systems’, Vol. 12, MIT Press, pp. 307-313.
41. Nelder, J. and Wedderburn, R. 1972, ‘Generalized linear models’, Journal of the Royal Statistical Society 135, 370-384.
42. Newey, W. and West, K. 1987, ‘A simple, positive semi-definite, het-eroscedasticity and autocorrelation consistent covariance matrix’, Econometrica 55, 703-708.
43. Orr, G. and Müller, K.-R. 1998, Neural Networks: Tricks of the Trade, Springer.
44. Rousseeuw, P. and Leroy, A. 1987, Robust Regression and Outlier Detection, John Wiley & Sons Inc.
45. Rumelhart, D., Hinton, G. and Williams, R. 1986, ‘Learning representations by back-propagating errors’, Nature 323, 533-536.
46. Schölkopf, B., Smola, A. and Müller, K.-R. 1998, ‘Nonlinear component analysis as a kernel eigenvalue problem’, Neural Computation 10, 1299-1319.
47. Takeuchi, I., Bengio, Y. and Kanamori, T. 2002, ‘Robust regression with asymmetric heavy-tail noise distributions’, Neural Computation 14, 2469-2496.
48. Tikhonov, A. and Arsenin, V. 1977, Solutions of Ill-posed Problems, W.H. Winston, Washington D.C.
49. Vapnik, V. 1998, Statistical Learning Theory, John Wiley, Lecture Notes in Economics and Mathematical Systems, volume 454.