Small area estimation of the insurance benefit for customer segmentations
Yeong‐Hwa Kim, Ki-Su Kim · Journal of the Korean Data and Information Science Society · 2009
Bayesian methods have been focused in recent years for solving small area estimation problems. In this paper, the hierarchical Bayes procedure is implemented via MCMC techniques and compared with the results of One-way, GLM-Normal, and GLM-Gamma cases by analyzing real data of insurance benefit for customer segmentations. After analyzing insurance benefit real data for customer segmentations, we can conclude that the insurance benefit estimator through the small area estimation is more efficient than the estimators by other methods. In addition, we found that the small area estimation gave accurate estimation result for the small number domains.