A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data
A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data
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DOI：
10.1080/00401706.2019.1647881
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发表时间：
2019-09
期刊：
影响因子：
2.5
通讯作者：
Simon Fontaine;Yi Yang;W. Qian;Yuwen Gu;Bo Fan
中科院分区：
文献类型：
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作者：
Simon Fontaine;Yi Yang;W. Qian;Yuwen Gu;Bo Fan
精算从业人员现在可以访问多个保险数据源，这些数据源对应于各种情况：多个业务线、伞式保险、多个危险等，尽管单目标方法的使用广泛且简单，但对这些类型的数据进行建模可能会受益于跨源联合执行变量选择的方法。我们提出了一个统一的算法来执行稀疏学习的Tweedie（复合泊松）模型下的这种融合的保险数据。通过整合多任务稀疏学习和稀疏Tweedie建模的思想，我们的算法产生了灵活的正则化，平衡了预测稀疏性和源间稀疏性。当应用于模拟和真实的数据时，我们的方法在预测和选择准确性方面明显优于单目标建模，特别是当源没有完全相同的预测因子时。在我们的R软件包MStweedie中提供了所提出的算法的有效实现，该软件包可在https://github.com/fontaine618/MStweedie上获得。本文的补充材料可在网上查阅。
Abstract Actuarial practitioners now have access to multiple sources of insurance data corresponding to various situations: multiple business lines, umbrella coverage, multiple hazards, and so on. Despite the wide use and simple nature of single-target approaches, modeling these types of data may benefit from an approach performing variable selection jointly across the sources. We propose a unified algorithm to perform sparse learning of such fused insurance data under the Tweedie (compound Poisson) model. By integrating ideas from multitask sparse learning and sparse Tweedie modeling, our algorithm produces flexible regularization that balances predictor sparsity and between-sources sparsity. When applied to simulated and real data, our approach clearly outperforms single-target modeling in both prediction and selection accuracy, notably when the sources do not have exactly the same set of predictors. An efficient implementation of the proposed algorithm is provided in our R package MStweedie, which is available at https://github.com/fontaine618/MStweedie. Supplementary materials for this article are available online.