社会健康保险精算中分布拟合模型的应用比较  

A comparative study on application of distribution fitting models in the actuary of Social Health Insurance

作　　者：蒋炜[1] 夏小亮[2] 吴昱[1] 李镒冲[1] 

机构地区：[1]中国疾病预防控制中心慢性非传染性疾病预防控制中心,北京100050 [2]中国疾病预防控制中心

出　　处：《公共卫生与预防医学》2015年第1期34-38,共5页Journal of Public Health and Preventive Medicine

摘　　要：目的比较多种右偏、厚尾的多参数分布模型与非参数核密度估计在社会健康保险精算中的应用价值。方法应用Gamma分布、Weibull分布、Pareto分布、对数正态分布、Log-logistic分布、Pearson-V分布、逆高斯分布以及非参数核密度估计方法分别对2004年某市某社保人群全年住院医疗赔付额的年龄别、性别数据进行分布拟合比较。结果非参数核密度估计方法的拟合效果远优于各参数分布拟合方法。结论非参数核密度估计对数据依赖程度低,表现稳健,拟合效果理想,在社会保险精算中应用值得推广。Objective To compare the performance between multi-parameters distribution fitting models with long thick right-side tails and non-parameter kernel density estimate in the actuary of social health insurance. Methods Gamma distribution,Weibull distribution,Pareto distribution,log-normal distribution,log-logisitc distribution,Pearson-V distribution,Inv-Gaussian distribution and Kernel Density Estimation（ KDE） were used to fit the distributions of claim data of a Chengdu hospitalization medical insurance plan respectively,and chi-square test was used to test the goodness-of-fit. Results The results of goodness-of-fit tests demonstrated that KDE performed much better than the parameter distribution models. Conclusion Research of actual data proved that KDE was a robust good-performance method,and the application of KDE in social health insurance actuary was recommendable.

关 键 词：社会健康保险精算 分布拟合 参数分布 核密度估计 

分 类 号：R18[医药卫生—流行病学]