Combining forward and backward mortality estimation.
Combining forward and backward mortality estimation.
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结合前向和后向死亡率估计。
DOI：
10.1080/00324728.2017.1319496
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Taylor,LowellJ
中科院分区：
文献类型：
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作者：
Black,DanA;Hsu,Yu-Chieh;Sanders,SethG;Taylor,LowellJ
人口统计学家经常通过结合两个数据源的信息来形成估计，当一个或两个数据源都不完整时，这是一个具有挑战性的问题。一个经典的例子是构建死亡概率，这需要对所研究的亚群进行死亡计数，并对相应的基础人口进行估计。方法通常需要“反向投影”，如在箭牌和斯科菲尔德的开创性分析的历史英语数据，或“反向”或“前向投影”所使用的李在他的重要重新分析的工作，都在20世纪80年代出版。我们的论文展示了如何使用广义矩量法（GMM）框架将向前和向后的方法进行优化组合。我们将该方法应用于美国相对较小的亚群（1930-39年出生的男性，按出生状态、出生队列、种族）的死亡概率估计，结合生命统计记录和人口普查样本的数据。
Demographers often form estimates by combining information from two data sources—a challenging problem when one or both data sources are incomplete. A classic example entails the construction of death probabilities, which requires death counts for the subpopulations under study and corresponding base population estimates. Approaches typically entail ‘back projection', as in Wrigley and Schofield's seminal analysis of historical English data, or ‘inverse’ or ‘forward projection’ as used by Lee in his important reanalysis of that work, both published in the 1980s. Our paper shows how forward and backward approaches can be optimally combined, using a generalized method of moments (GMM) framework. We apply the method to the estimation of death probabilities for relatively small subpopulations within the United States (men born 1930–39 by state of birth by birth cohort by race), combining data from vital statistics records and census samples.