多人口死亡率建模与预测：带时间加权的多元函数主成分分析方法
Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches
AI总结：
本文提出两种基于多元函数主成分分析的多人口死亡率联合建模与预测新模型，利用多国性别数据验证表明，所提协同建模方法在预测精度和性质上均优于现有模型。
摘要：
密切相关人口的人类死亡率模式和轨迹可能相互关联并具有相似性。在考虑其异质性的同时，将它们同时进行建模始终是理想的选择。本文引入了两种新模型，通过对多元函数主成分分析技术进行改进，用于多子人口的联合死亡率建模与预测。第一个模型将独立函数数据模型扩展到多人口建模设置中。在第二个模型中，我们提出了一种用于协同建模的新型多元函数主成分方法。其设计主要实现了这样一个理念：当若干子人口群体具有相似的社会经济条件或共同的生物学特征时，这种密切联系预计将以非发散的方式演变。我们使用按性别划分的死亡率数据演示了所提出的方法。通过与十个发达国家的死亡率数据进行比较，进一步将它们的预测表现与几种现有模型进行了对比，包括独立函数数据模型和 Product-Ratio 模型。实验结果表明，所提出的第一种模型保持了与现有方法相当的预测能力。相比之下，所提出的第二种模型在预测精度方面优于第一种模型以及现有模型，此外还具备若干理想性质。
英文摘要：
Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This paper introduces two new models for joint mortality modelling and forecasting multiple subpopulations in adaptations of the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multi-population modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics, such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. Our experiment results show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the current models in terms of forecast accuracy, in addition to several desirable properties.
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