Efficient empirical Bayes estimates for risk parameters of Pareto distributions

Yongmei Du, Zhouping Li and Xiaosong Chen

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 6, 1674-1692

Abstract: Pareto distributions are useful for modeling the loss data in many fields such as actuarial science, economics, insurance, hydrology and reliability theory. In this paper, we consider the simultaneous estimation of the risk parameters of Pareto distributions from the perspective of empirical Bayes, novel SURE-type shrinkage estimators are developed by employing the Stein’s unbiased estimate of risk (SURE). Specifically, due to the lacking of the analytic form for the risk function, we propose to estimate the hyperparameters by minimizing an unbiased estimate of an approximation of the risk function. Under mild conditions, we prove the optimality of the new shrinkage estimators. The performance of our estimators is illustrated with simulation studies and an analysis of a real auto insurance claim dataset.

Date: 2022
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DOI: 10.1080/03610926.2020.1766501

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