Predicting Insurance Losses Under Cross-Classification: A Comparison of Alternative Approaches
回顾了多种汽车保险定价的数学模型，基于两个州不同时期的数据比较其预测能力，发现经验贝叶斯方法预测效果最佳。
Various mathematical and statistical models for estimation of automobile insurance pricing are reviewed. The methods are compared on their predictive ability based on two sets of automobile insurance data for two different states collected over two different periods. The issue of model complexity versus data availability is resolved through a comparison of the accuracy of prediction. The models reviewed range from the use of simple cell means to various multiplicative-additive schemes to the empirical-Bayes approach. The empirical-Bayes approach, with prediction based on both model-based and individual cell estimates, seems to yield the best forecast.