Increasingly during the past decade, researchers have sought to leverage auxiliary data for enhancing individualized inference. Many existing methods, such as multisource exchangeability models (MEM), have been developed to borrow information from multiple supplemental sources to support parameter inference in a primary source. MEM and its alternatives decide how much information to borrow based on the exchangeability of the primary and supplemental sources, where exchangeability is defined as equality of the target parameter. Other information that may also help determine the exchangeability of sources is ignored. In this article, we propose a generalized Reinforced Borrowing Framework (RBF) leveraging auxiliary data for enhancing individualized inference using a distance-embedded prior which utilizes data not only about the target parameter, but also uses different types of auxiliary information sources to "reinforce" inference on the target parameter. RBF improves inference with minimal additional computational burden. We demonstrate the application of RBF to a study investigating the impact of the COVID-19 pandemic on individual activity and transportation behaviors, where RBF achieves 20-40% lower MSE compared with existing methods.

翻译：过去十年间，研究者日益寻求通过辅助数据增强个性化推断。许多现有方法（如多源可交换性模型MEM）被开发用于从多个补充源借力信息，以支持主源的参数推断。MEM及其替代方法基于主源与补充源的可交换性（即目标参数相等性）来决定信息借力程度，而其他可能有助于判定源可交换性的信息则被忽略。本文提出一种广义强化借力框架（RBF），通过距离嵌入先验利用辅助数据增强个性化推断——该先验不仅使用目标参数数据，还利用不同类型的辅助信息源"强化"目标参数的推断。RBF在最小化额外计算负担的同时提升了推断性能。我们将RBF应用于一项研究COVID-19疫情对个体活动与交通行为影响的研究，结果显示与现有方法相比，RBF均方误差降低20%-40%。