A New Class of Composite Gbii Regression Models with Varying Threshold for Modelling Heavy-Tailed Data
A New Class of Composite Gbii Regression Models with Varying Threshold for Modelling Heavy-Tailed Data
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DOI：
10.2139/ssrn.4087142
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Zhengxiao Li;Fei Wang;Zhengtang Zhao
中科院分区：
文献类型：
--
作者：
Zhengxiao Li;Fei Wang;Zhengtang Zhao
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.
