A Bayesian Approach to Combining Information from a Census, a Coverage Measurement Survey, and Demographic Analysis
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AI 根据所提供的论文材料归类：研究重点是人口统计信息融合和调查计数，摘要未提供面板、因果或其他所列细分方向的明确证据。
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融合人口普查、覆盖调查与人口学分析的贝叶斯方法
- 研究问题
- 如何结合人口普查、捕获—再捕获覆盖调查和按年龄及种族分层的性别比信息，改善存在漏计问题的分组人口估计？
- 方法与思路
- 作者提出选择信息融合模型的一般原则，再将选定模型嵌入针对人口后分层计数的贝叶斯框架，进行平滑和不确定性处理。
- 材料陈述的贡献
- 摘要称方法可处理负计数、减少孤立异常调整，并检验负计数是否可归于抽样变异；应用涉及美国一九九〇年人口普查。
- 阅读关注点
- 应核对模型选择原则、不同信息源之间的依赖假设、后分层平滑方式，以及区分抽样变异与系统偏差的检验依据。
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- OpenAlex 索引摘要未注明稿件版本，不能推断刊本变化；摘要强调部分建模假设难由拟合检验，也未给出实际改进幅度。
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.