| Reference: | lan, E.G., Adrian, F.M., and Tai-Ming L., Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling, Journal of the American Statistical Association 87, 1992, pp.523-532.
Bowers, N., Gerber, H., Hickman, J., Jones, D. and Nesbitt, C., Actuarial Mathematics, Chicago: Society of Actuaries, 1986.
Buhlmann,H., Experience Rating and Credibility, ASTIN Bulltin 4, 1967, pp.199-207.
Buhlmann,H., Experience Rating and Credibility, ASTIN Bulltin 5, 1969, pp.157-165.
Buhlmann, H., and Straub, E., Credibility for Loss Ratios, English translation in ARCH, 1972.
Chang, S.C., Simple Bayes method in estimating the claim size of the insured risks, Insurance Monograph, 43, 1996, pp.160-165.
Chang, S.C., Using Monte Carlo Markov chain method to analyze the loss distributions under various risks, Insurance Monograph, 44, 1997, pp.40-51.
David, P.M., A Bayesian analysis of a simultaneous equations model for insurance rate-making, Insurance: Mathematics and Economics, 12, 1993, pp.265-286.
Daykin, C.D., T. and Pesonen, M., Practical Risk Theory for Actuauies, London: Chapman & Hall, Inc, 1994.
Geisser, S., Predictive Inference: An Introduction, London: Chapman & Hall, Inc, 1993.
Gilk, W. R., Markov Chain Monte Carlo in Practice, London: Chapman & Hall, Inc, 1996.
Gelfand, A.E., and Smith, A.F.M., Sampling based approaches to calculating matginal densities, Journal of the American Statistical Association, 85, 1990, pp.398-409.
Grandell, J., Aspects of Risk Theory, New York: Springer Verlag, Heidelberg, 1991.
Geyer, C.J., and Thompson, E.A. Annealing Markov chain Monte Carlo with applications to pedigree analysis, Journal of the American Statistical Association, 1995.
Hastings, W.K., Monte Carlo sampling methods using Markov chains and their applications, Biomebrika 57, 1970, pp.97-109.
Herzog, T., An Introduction to Bayesian Credibility and Related Topics, Part 4 Study Note, New York: Casualty Actuarial Society.
Hogg, R.V. and Klugman, S.A., Loss Distributions, New York: Wiley, 1984.
Huebner, S.S., Black, K. and Cline, R., PROPERTY AND LIABILITY INSURANCE, America: Prentice Hall, Inc, 1982.
James, O.B., and Ming-Hui C., Predicting retirement patterns: prediction for a multinomial distribution with constrained parameter space, The Statistician 42, 1993, pp.427-443.
Johnson, V.E., Studying Convergence of Markov Chain Monte Carlo Algorithms Using Coupled Sample Paths, Journal of the American Statistical Association 91, 1996, pp.155-166.
Klugman, S.A., Bayesian Statistics in Actuarial Science, America: Kluwer Academic Publishers, 1992.
Liu, C. H., Pricing for the catastrophic future option, unpublished master thesis, National Chengchi University, Department of Risk Management and Insurance, 1997.
Polson, N.G., Convergence of Markov chain Monte Carlo algorithms, Bayesian Statistic 5, Oxford: Oxford University Press, 1995.
Parmenter, M., Theory of Interest and Life Contingencies, with Pension Applications: A Problem Solving Approach, Winsted, CT: ACTEX, 1988.
Robert, C., THE BAYESIAN CHOICE, New York: Springer Verlag, Heidelberg, 1994.
Rubin, D. B., "Using the SIR Algorithm to Simulate Posterior Distributions," in Bayesian Statistics 3, eds. Bernardo, J. M., De Groot, M. H., Lindley, D. V., and Smith, A. F. M., New York: Oxford University Press, 1988.
S-PLUS Programmer`s Manual, Version 3.3. StatSci, MathSoft Inc. Seattle, Washington, 1989.
Stewart, L.T., Hierarchical Bayesian analysis using Monte Carlo integration: computing posterior distributions when there are many possible models, The Statistician 36, 1987, pp.211-219.
Tierney, L., Exploring posterior distributions using Markov chains. Computer Science and Statistics, 1991, pp.563-570.
Tanner, M.A., and Wong, W.H., The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association, 82, 1995, pp.528-540.
Van Dijk, H.K., Peter Hop, J., and Louter, A.S., An algorithm for the computation of posterior moments and densities using simple importance sampling, The Statistician 36, 1987, pp.83-90. |
