English(EN) Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting

新的提升方法模拟保险索赔频率和严重性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新的提升方法，用于拟合结构化加性分布联结回归模型。该方法旨在联合模拟保险索赔的频率和严重性，将广义加性模型（GAMLSS）扩展到允许多个分布参数依赖于协变量。与黑箱模型相比，所提出的框架提供了更高的透明度和可审计性，并在合成数据和真实世界数据上展示了具有竞争力的预测性能。 AI

影响这项研究引入了一种更具可解释性和透明度的统计建模方法，有望改善保险领域的风险评估。

排序理由该集群包含一篇详细介绍新统计建模方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv stat.ML TIER_1 English(EN) · Banghee So, Na Zhang · 2026-10-28 04:00

联合分布Copula回归用于保险索赔频率与严重性通过Boosting

arXiv:2610.36835v1 Announce Type: new Abstract: In this paper, we propose a model-based boosting approach for fitting structured additive distributional copula regression models with bivariate responses, focusing particularly on the joint modeling of claim frequency and claim sev…