多种人群死亡率建模和预测:加权多变量功能主要组件方法
1School of Mathematics and Actuarial Science, University of Leicester, Leicester, UK.
Journal of applied statistics
|November 16, 2023
概括
本研究引入了两种新的模型,用于在多个子群体中共同预测死亡率. 第二种模型使用多变量功能主要组件分析,比现有方法显著提高了预测准确度.
科学领域:
- 人口统计学 人口统计学
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 跨相关人口的人类死亡率模式往往表现出相似之处.
- 同时对多个子群体进行建模是可取的,但由于异质性而具有挑战性.
- 现有的方法可能无法完全捕捉到相关群体中死亡率的连贯演变.
研究的目的:
- 引入新型模型,在多个子群体中进行联合死亡率建模和预测.
- 开发一种多变量功能主要成分分析 (MFPCA) 方法,用于连贯的死亡率建模.
- 通过使用现实世界死亡率数据,将新模型的性能与已建立的方法进行比较.
主要方法:
- 将独立的功能数据模型扩展到多人群环境中.
- 开发一种用于连贯建模的新型多变量功能主要组件方法.
- 使用十个发达国家的性别特定死亡率数据进行应用和比较.
主要成果:
- 第一个提出的模型显示了与现有方法相比较的预测能力.
- 基于MFPCA的第二个拟议模型在预测准确性方面明显优于第一个模型和现有方法.
- 新的MFPCA方法有效地模拟了相关亚群中死亡率的非分歧演变.
结论:
- 拟议的多变量功能主要组件方法为联合死亡率预测提供了一种优越的方法.
- 准确的死亡率预测对于公共卫生,政策制定和精算科学至关重要.
- 该研究强调了在对人口的死亡率建模时考虑相互依赖和共同特征的重要性.
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