Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data
Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data
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DOI：
10.1017/pan.2019.4
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发表时间：
2019-07-01
影响因子：
5.4
通讯作者：
Wang, Mallory
中科院分区：
文献类型：
--
作者：
Caughey, Devin;Wang, Mallory
社会科学家经常对人口如何随时间演变感兴趣。例如，为调查创建分层后权重需要有关目标人群中加权变量联合分布的信息。然而，通常情况下，不同时期的人口数据很少。即使观察到人口数据，数据的内容和结构（观察到的变量以及它们的边际分布或联合分布是否已知）也会随时间而变化，从而无法进行直接插值。因此，调查权重通常仅基于辅助变量的小子集，随着时间的推移定期观察其联合总体分布，因此无法充分利用辅助信息。为了解决这个问题，我们开发了一个动态贝叶斯生态推理模型，用于根据稀疏、不规则和噪声数据的边缘（或部分联合）分布来估计多元分类分布。我们的方法结合了（1）以未观察到的细胞比例为条件的观察到的边缘的狄利克雷采样模型； (2)一组编码不同人口数量之间逻辑关系的方程； (3) 针对特定时期比例的狄利克雷转移模型，该模型汇集了跨时期的信息。我们根据 1930 年至 1960 年间不定期获得的人口数据，按种族和地区估算美国电话年拥有率，以此来说明这种方法。这种方法可能在学者们希望根据边际数据对内部细胞做出动态生态推断的各种情况下有用。新的 R 包 estsubpop 实现了该方法。
Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables' joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data-which variables are observed and whether their marginal or joint distributions are known-differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.