论文标题
一类新的复合GBII回归模型，具有不同的阈值，用于建模重尾数据
A new class of composite GBII regression models with varying threshold for modelling heavy-tailed data
论文作者
论文摘要
已经提出了第二类（GBII）的四参数beta分布，用于对具有重尾功能的保险损失进行建模。本文的目的是通过使用模式匹配方法剪辑两个GBII分布来介绍参数复合GBII回归建模。它旨在同时建模大小主张，并通过将协变量引入位置参数来捕获保单持有人的异质性。在这种情况下，分配两个GBII分布的门槛根据个人的风险特征而在个人保单持有人之间有所不同。所提出的回归建模还包含广泛的保险损失分布，分别是头部和尾巴，并为参数估计和模型预测提供了近距离的表达式。进行了仿真研究，以显示提出的估计方法的准确性和回归的灵活性。与文献中竞争模型的结果相比，提供了丹麦火灾损失数据集和中国医疗保险索赔数据集的一些新类别分布和回归的适用性。
The four-parameter generalized beta distribution of the second kind (GBII) has been proposed for modelling insurance losses with heavy-tailed features. The aim of this paper is to present a parametric composite GBII regression modelling by splicing two GBII distributions using mode matching method. It is designed for simultaneous modeling of small and large claims and capturing the policyholder heterogeneity by introducing the covariates into the location parameter. In such cases, the threshold that splits two GBII distributions varies across individuals policyholders based on their risk features. The proposed regression modelling also contains a wide range of insurance loss distributions as the head and the tail respectively and provides the close-formed expressions for parameter estimation and model prediction. A simulation study is conducted to show the accuracy of the proposed estimation method and the flexibility of the regressions. Some illustrations of the applicability of the new class of distributions and regressions are provided with a Danish fire losses data set and a Chinese medical insurance claims data set, comparing with the results of competing models from the literature.
