发表机构
Nanyang Business School, Nanyang Technological University（南洋商学院，南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Dirichlet混合成员模型（DMMM），将多元分布信度扩展至条件均值之外，通过潜在风险类别组合个体经验与组合信息，实现精确后验预测并应用于车辆远程信息处理。
AI 中文摘要
信度理论将个体经验与投资组合信息相结合用于保险定价，但经典表述主要关注条件均值和期望保费。我们提出了一种用于多元分布信度的Dirichlet混合成员模型（DMMM）。投保人风险由潜在风险类别上的稳定组成表示：基线特征决定其先验评估，而重复的多元经验逐步更新其后验评估。支持特定的专家分布适应异质性结果，通过共享潜在结构引入依赖性。由此得到的后验预测分布可以精确地写成投资组合和经验成分的凸组合，将熟悉的信度因子结构扩展到条件均值之外。我们通过模拟实验和车辆远程信息处理应用研究了该框架。模拟表明，相对简单的专家可以捕获复杂的多元保险分布，并随着经验积累学习投保人特定风险。在远程信息处理应用中，近期索赔和驾驶行为为未来索赔频率预测提供了互补信息，使相似的投保人能够获得不同的经验评级评估。因此，DMMM提供了一个灵活且可解释的框架，用于组合异构保险经验，同时保留经典信度的结构。
英文摘要
Credibility theory combines individual experience with portfolio information for insurance pricing, but classical formulations focus primarily on conditional means and expected premiums. We propose a Dirichlet mixed-membership model (DMMM) for multivariate distributional credibility. Policyholder risk is represented by a stable composition over latent risk classes: baseline characteristics determine its a priori assessment, while repeated multivariate experience progressively updates its a posteriori assessment. Support-specific expert distributions accommodate heterogeneous outcomes, with dependence induced through the shared latent structure. The resulting posterior predictive distribution can be written exactly as a convex combination of portfolio and experience components, extending the familiar credibility-factor structure beyond the conditional mean. We study the framework through a simulation experiment and a vehicle telematics application. The simulation shows that relatively simple experts can capture complex multivariate insurance distributions and learn policyholder-specific risk as experience accumulates. In the telematics application, recent claims and driving behaviour provide complementary information for future claim-frequency prediction, allowing similar policyholders to receive different experience-rated assessments. The DMMM therefore provides a flexible and interpretable framework for combining heterogeneous insurance experience while retaining the structure of classical credibility.
