标题： 基于高频数据的梯度提升多种群死亡率建模 显示英文标题

标题： Gradient boosted multi-population mortality modelling with high-frequency data

摘要： 高频死亡率数据仍然是一个研究不足但至关重要的研究领域。 虽然其分析可以揭示气候极端事件的短期健康影响并实现更及时的死亡率预测，但其复杂的时间结构对传统死亡率模型构成了重大挑战。 为了发挥高频死亡率数据的潜力，本文在多人口背景下将梯度提升技术与传统随机死亡率模型相结合。 我们的主要创新在于在梯度提升框架中使用Li和Lee模型作为弱学习器，取代传统的决策树。 实证研究使用了30个国家（人类死亡率数据库，2015--2019）的周死亡率数据。 所提出的方法不仅通过准确捕捉潜在死亡率趋势和季节性模式提高了模型拟合度，而且相比基准模型实现了更高的预测准确性。 我们还探讨了多人口死亡率建模中的一个关键挑战：如何选择具有足够相似死亡率经验的子人群。 基于死亡率改善率和季节强度进行了全面的聚类分析。 结果表明了我们所提出模型的稳健性，在不同的聚类配置下都能保持稳定的预测准确性。 显示英文摘要

摘要： High-frequency mortality data remains an understudied yet critical research area. While its analysis can reveal short-term health impacts of climate extremes and enable more timely mortality forecasts, its complex temporal structure poses significant challenges to traditional mortality models. To leverage the power of high-frequency mortality data, this paper introduces a novel integration of gradient boosting techniques into traditional stochastic mortality models under a multi-population setting. Our key innovation lies in using the Li and Lee model as the weak learner within the gradient boosting framework, replacing conventional decision trees. Empirical studies are conducted using weekly mortality data from 30 countries (Human Mortality Database, 2015--2019). The proposed methodology not only enhances model fit by accurately capturing underlying mortality trends and seasonal patterns, but also achieves superior forecast accuracy, compared to the benchmark models. We also investigate a key challenge in multi-population mortality modelling: how to select appropriate sub-populations with sufficiently similar mortality experiences. A comprehensive clustering exercise is conducted based on mortality improvement rates and seasonal strength. The results demonstrate the robustness of our proposed model, yielding stable forecast accuracy under different clustering configurations.