A Bayesian Approach to Combining Information from a Census, a Coverage Measurement Survey, and Demographic Analysis
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融合人口普查、覆盖测量调查与人口学分析的贝叶斯方法
- 研究问题
- 如何融合人口普查、捕获再捕获覆盖调查和按年龄种族分层的性别比信息，改善人口估计并处理负计数与异常调整？
- 方法与思路
- 作者提出模型选择的一般原则，据此选定信息融合模型，并将其嵌入后分层人口计数的更完整贝叶斯模型。
- 材料陈述的贡献
- 摘要称，方法可处理负估计计数、平滑后分层估计并检验负计数成因；还可扩展纳入人口学等信息来源的不确定性。
- 阅读关注点
- 应核对难以由拟合检验的信息融合假设、模型选择原则和不确定性传播，以及1990年普查应用与既有方法的比较。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；摘要强调关键建模假设难以由拟合评估，且未给出具体假设、检验细节或比较数值。
原文摘要
Demographic analysis of data on births, deaths, and migration and coverage measurement surveys that use capture-recapture methods have both been used to assess U.S. Census counts. These approaches have established that unadjusted Census counts are seriously flawed for groups such as young and middle-aged African-American men. There is considerable interest in methods that combine information from the Census, coverage measurement surveys, and demographic information to improve Census estimates of the population. This article describes a number of models that have been proposed to accomplish this synthesis when the demographic information is in the form of sex ratios stratified by age and race. A key difficulty is that methods for combining information require modeling assumptions that are difficult to assess based on fit to the data. We propose some general principles for aiding the choice among alternative models. We then pick a particular model based on these principles and imbed it within a more comprehensive Bayesian model for counts in poststrata of the population. Our Bayesian approach provides a principled solution to the existence of negative estimated counts in some subpopulations; provides for smoothing of estimates across poststrata, reducing the problem of isolated outlying adjustments; allows a test of whether negative cell counts are due to sampling variability or more egregious problems such as bias in Census or coverage measurement survey counts; and can be easily extended to provide estimates of precision that incorporate uncertainty in the estimates from demographic analysis and other sources. The model is applied to data for African-American age 30–49 from the 1990 Census, and results are compared with those from existing methods.