AI 中文总结
该研究提出整合调查、汇总数据与选择校正的贝叶斯层次框架，含三类模型，经模拟和爱沙尼亚选举数据验证，可提升小区域选民投票率估计精度。
AI 中文摘要
对人口统计子群体进行准确的小区域选民投票率估计，是政治分析的关键，但在方法上面临挑战。调查数据存在过度报告、非代表性及不可忽略的无应答问题，而来自汇总数据的生态推断（EI）易受生态谬误影响。我们提出一种贝叶斯层次框架，该框架联合整合个体层面调查数据、官方汇总投票率差值及普查单元格计数。该框架包含三个模型：多水平生态推断（EI）模型，将MRP式结构扩展至汇总数据；调查与差值联合拟合（PM）模型，以一致似然函数同时适配调查与差值；全选择（FS）模型，在随机接触假设下加入赫克曼（Heckman）式选择校正，并使用有信息先验以实现识别。在基于经验普查人口统计数据、受控选择机制的模拟中，FS模型较现有基准模型有显著提升，尤其在强选择偏差或严重删失情况下表现更佳，但对未建模的选择噪声较为敏感。在2023年爱沙尼亚议会选举的应用中，PM模型在可用验证差值上的表现与当前最优方法相当，FS模型则呈现出适度提升，且该提升依赖于有信息先验。
英文摘要
Accurate small-area estimation of voter turnout for demographic subgroups is crucial for political analysis but methodologically challenging. Survey data suffer from over-reporting, non-representativeness, and non-ignorable non-response, while ecological inference (EI) from aggregate data is vulnerable to the ecological fallacy. We propose a Bayesian hierarchical framework that jointly integrates individual-level survey data, official aggregate turnout margins, and census cell counts. The framework comprises three models: a multilevel ecological inference (EI) model that extends MRP-style structure to aggregate data, a Poll-and-Margin (PM) model that jointly fits survey and margins in one coherent likelihood, and a Full Selection (FS) model that adds a Heckman-style selection correction under a random-contact assumption and uses informative priors for identification. In simulations with controlled selection mechanisms based on empirical census demographics, FS substantially improves over established benchmarks, particularly under strong selection bias or heavy censoring, but is sensitive to unmodeled selection noise. In an application to the 2023 Estonian parliamentary election, PM performs similarly to the current state of the art on available validation margins, with FS showing a modest improvement that depends on the informative priors.
Comments18 pages + appendices
