摘要
汽车保险定价的基础在于风险分析,车辆、驾驶人以及行车环境等因素构成汽车保险定价所倚赖的一个风险系统.本文引入广义加法模型(GAM).将非参数平滑方法应用于到GAM中,结合贝叶斯理论(Bayes)和马尔可夫蒙特卡罗(MCMC)方法得到参数估计,构建汽车保险定价模型,并以国外某保险数据为样本进行实证分析,得到了较好的效果.
The basic Principle on car insurance pricing is risk analysis. The conditious of car, the car drivers,and circumference of driving make a whole risk system of car insurance pricing. We introduce the Generalized Addictiv Model to car insurance pricing as a starting point. GAM applies the nonparametric smoothing to estimates parameter, combined with bayes theory and MCMC methods, which construct car insurance pricing model to a foreign insurance data.
出处
《数学的实践与认识》
CSCD
北大核心
2011年第17期64-69,共6页
Mathematics in Practice and Theory
关键词
汽车保险定价
GAM
贝叶斯
MCMC
car insurance pricing
generalized linear model
bayes theory
markov chainmonte carlo simulation
