We present and apply methodology to improve inference for small area parameters by using data from several sources. This work extends Cahoy and Sedransk (2023) who showed how to integrate summary statistics from several sources. Our methodology uses hierarchical global-local prior distributions to make inferences for the proportion of individuals in Florida's counties who do not have health insurance. Results from an extensive simulation study show that this methodology will provide improved inference by using several data sources. Among the five model variants evaluated the ones using horseshoe priors for all variances have better performance than the ones using lasso priors for the local variances.

翻译：本文提出并应用了一种方法学，通过整合多个数据源来改进小区域参数的推断。本研究扩展了Cahoy和Sedransk（2023）提出的整合多源汇总统计量的方法。我们采用分层全局-局部先验分布，对佛罗里达州各县无医疗保险人口比例进行推断。大量模拟研究结果表明，该方法通过利用多源数据能够提供更优的推断效果。在评估的五种模型变体中，对所有方差使用马蹄先验的模型在性能上优于对局部方差使用套索先验的模型。