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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究人口健康寿命与生命表估计,没有分位数效应、因果推断或其他所列细分方向的明确证据。

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关注这篇论文的作者
Kosuke Imai
Samir Soneji

无残疾预期寿命的估计

研究问题
广泛使用的 Sullivan 无残疾预期寿命估计依赖哪些条件,其统计性质能否得到论证,并扩展到不满足平稳性的情况?
方法与思路
作者为 Sullivan 方法建立统计基础,在平稳性假设下分析点估计和方差估计,并利用队列生命表与调查资料构造扩展。
材料陈述的贡献
摘要称平稳条件下估计量无偏且一致,标准方差估计一致且近似无偏,并说明具备相应队列和调查数据时可放宽平稳性。
阅读关注点
应核对死亡与残疾过程的平稳性定义、方差推导、非平稳扩展的数据要求,以及美国出生队列分析的解释范围。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明;不能推断其他版本变化。摘要未给出平稳性的完整条件,历史队列发现不能直接推广到其他人口。

原文摘要

A rapidly aging population, such as the United States today, is characterized by the increased prevalence of chronic impairment. Robust estimation of disability-free life expectancy (DFLE), or healthy life expectancy, is essential for examining whether additional years of life are spent in good health and whether life expectancy is increasing faster than the decline of disability rates. Over 30 years since its publication, Sullivan's method remains the most widely used method to estimate DFLE. Therefore, it is surprising to note that Sullivan did not provide any formal justification of his method. Debates in the literature have centered around the properties of Sullivan's method and have yielded conflicting results regarding the assumptions required for Sullivan's method. In this article we establish a statistical foundation of Sullivan's method. We prove that, under stationarity assumptions, Sullivan's estimator is unbiased and consistent. This resolves the debate in the literature, which has generally concluded that additional assumptions are necessary. We also show that the standard variance estimator is consistent and approximately unbiased. Finally, we demonstrate that Sullivan's method can be extended to estimate DFLE without stationarity assumptions. Such an extension is possible whenever a cohort life table and either consecutive cross-sectional disability surveys or a longitudinal survey are available. Our empirical analysis of the 1907 and 1912 U.S. birth cohorts suggests that while mortality rates remain approximately stationary, disability rates decline during this time period.

笔记与分组
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