AI 中文总结
研究针对分组风暴损失数据，假设潜在零膨胀混合效应空间点过程框架，推导模型处理不平衡多变量零膨胀计数数据，纳入高分辨率预测变量，强调其在解决风暴损失局部异质性上的价值。
AI 中文摘要
第三方天气和暴露信息粒度增加使保险公司能更有效预测天气相关损失。但损失结果常按空间分组观测报告。假设共同风暴引发索赔的潜在零膨胀混合效应空间点过程框架，我们推导一个模型用于不平衡多变量零膨胀计数数据，纳入高空间粒度观测的丰富天气和暴露预测变量以预测索赔模式。该模型考虑共同风暴影响地点在超额零值以及联合索赔计数中的依赖性。利用不动产暴露和损失数据，我们强调纳入粒度预测变量以解决风暴损失局部异质性的价值。
英文摘要
The increasing granularity of third-party weather and exposure information can allow insurers to more effectively predict weather-related losses. However, loss outcomes are often reported in spatially grouped observations, such as at the county level, so higher resolution predictors are aggregated to align with the granularity of the outcome in standard analyses. Assuming an underlying zero-inflated mixed-effects spatial point process framework for claims arising from a common storm, we derive a model for unbalanced, multivariate zero-inflated count data that incorporates rich weather and exposure predictors observed at higher spatial granularity to predict claim patterns. The model accommodates the dependence between locations affected by a common storm in the excess zeros, as well as in the joint claim counts. Using real property exposure and loss data, we emphasize the value of incorporating granular predictors to address the localized heterogeneity of storm losses.
Journal refNorth American Actuarial Journal (2026)
DOI：10.1080/10920277.2026.2720934
