零膨胀负二项回归模型的推广与费率厘定

Generalization of zero-inflated negative binomial regression model and ratemaking

在费率厘定中,当索赔次数数据存在过离散(over-dispersion)特征时,通常会采用负二项回归模型,但当索赔数据中同时又出现零膨胀(zero-inflated)问题时,负二项回归模型不再适合对这样的数据进行分析.在传统的零膨胀负二项回归模型为基础,并将其推广到更为一般的形式,同时利用解决费率厘定中出现的既有过离散又有零膨胀的问题.通过对一组汽车损失数据的拟合,推广后的零膨胀负二项回归模型有效地改善了拟合效果.

When the claim numbers appear to be over-dispersed in ratemaking, negative binomial regres- sion model will be usually applied. However, it is also possible that the claim numbers may be zero-inflated, and then the negative binomial regression is not suitable for those data. The paper makes generalization of zero-inflated negative binomial distribution based on traditional ones to deal with the over-dispersed and zero-inflated data simultaneously. At the end of set of automobile insurance loss and the result shows the paper, the extended model is applied to a data that the goodness-of-fit can be effectively improved.