死亡率下降的年龄模式轮换:统计证据和模型

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
J. S. Li, Joseph H. T. Kim
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引用次数: 0

摘要

在死亡率预测方面,“轮换”指的是死亡率下降在老年时加速,而在年轻时减慢的现象。由于自转通常是微妙的,因此很难用统计数据驱动的方式进行确认和建模。在本文中,我们试图通过提出一种替代的建模方法来克服这一挑战。该方法包含一个新的模型结构,其中包括一个专门用于测量旋转的组件。它还具有一种被称为ANCOVA的建模技术,它允许我们统计地检测旋转并推断未来的现象。我们提出的方法得出的死亡率预测与Li等人的预测相似。[扩展Lee-Carter方法,为死亡率下降的年龄模式轮换建模,用于长期预测。]人口学50(6),2037-205,并且可能被认为比Li等人的方法更有利,因为它不仅能够生成静态预测,而且能够生成随机预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rotation in age patterns of mortality decline: statistical evidence and modeling
In the context of mortality forecasting, “rotation” refers to the phenomenon that mortality decline accelerates at older ages but decelerates at younger ages. Since rotation is typically subtle, it is difficult to be confirmed and modeled in a statistical, data-driven manner. In this paper, we attempt to overcome this challenge by proposing an alternative modeling approach. The approach encompasses a new model structure, which includes a component that is devoted to measuring rotation. It also features a modeling technique known as ANCOVA, which allows us to statistically detect rotation and extrapolate the phenomenon into the future. Our proposed approach yields plausible mortality forecasts that are similar to those produced by Li et al. [Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography 50 (6), 2037–205, and may be considered more advantageous than the approach of Li et al. in the sense that it is able to generate not only static but also stochastic forecasts.
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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