摘要
Bhlmann模型是贝叶斯方法在经验费率厘定中最为著名的应用,然而该模型在结构参数先验信息不足的情况下,并不能得出参数的无偏后验估计。本文针对传统方法的不足,运用基于MCMC模拟的贝叶斯方法对历史数据进行校正,通过Gibbs抽样构造出一种多层Poisson模型稳态分布的马尔可夫链,动态模拟出索赔频率的后验分布以及缺失参数值的后验估计,改进了传统的索赔校正模型,提高了计算的精度。利用WinBUGS软件包进行建模分析,证明了该模型的直观性与有效性。
Bühlmann model is the most famous application of the Bayesian method for the experience rate making. However, by this model one cannot get the unbiased posterior estimation of the parameters when there is not sufficient prior information for the structural parameters. Aimed at the fault of the traditional methods, this paper discusses how to conduct a Markov Chain for a hierarchical Poisson model with Gibbs sampling by applying Bayesian approach to revise the his tory data and get the posterior distribrtions of the claim frequency as well as the posterior estimation of the censoring parameters dynamically, as well as improve the precision of the numeration. Also this paper utilizes the WinBUGS package, which is based on the MCMC method, to prove the objebtivity and validity of the model.
出处
《数量经济技术经济研究》
CSSCI
北大核心
2005年第10期92-99,共8页
Journal of Quantitative & Technological Economics
基金
中国博士后科学基金项目(20040350216)
国家社科基金项目(04CTJ003)。
