Researchers are increasingly interested in using sensor technology to collect accurate activity information and make individualized inference about treatments, exposures, and policies. How to optimally combine population data with data from an individual remains an open question. Multi-source exchangeability models (MEMs) are a Bayesian approach for increasing precision by combining potentially heterogeneous supplemental data sources into analysis of a primary source. MEMs are a potentially powerful tool for individualized inference but can integrate only a few sources; their model space grows exponentially, making them intractable for high-dimensional applications. We propose iterated MEMs (iMEMs), which identify a subset of the most exchangeable sources prior to fitting a MEM model. iMEM complexity scales linearly with the number of sources, and iMEMs greatly increase precision while maintaining desirable asymptotic and small sample properties. We apply iMEMs to individual-level behavior and emotion data from a smartphone app and show that they achieve individualized inference with up to 99% efficiency gain relative to standard analyses that do not borrow information. This article is protected by copyright. All rights reserved.

中文翻译：

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用于个性化推理的迭代多源可交换模型与移动传感器数据的应用

研究人员越来越有兴趣使用传感器技术来收集准确的活动信息，并对治疗、暴露和政策进行个性化推断。如何将人口数据与个人数据最佳结合仍然是一个悬而未决的问题。多源可交换性模型 (MEM) 是一种贝叶斯方法，通过将潜在的异构补充数据源组合到主要源的分析中来提高精度。MEMS 是个性化推理的潜在强大工具，但只能集成少数来源；它们的模型空间呈指数增长，这使得它们难以处理高维应用程序。我们提出了迭代 MEMs (iMEMs)，它在拟合 MEM 模型之前识别出最易交换源的子集。iMEM 复杂性与源数量呈线性关系，和 iMEM 极大地提高了精度，同时保持了理想的渐近和小样本特性。我们将 iMEM 应用于来自智能手机应用程序的个人层面的行为和情绪数据，并表明与不借用信息的标准分析相比，它们实现了个性化推理，效率提高了 99%。本文受版权保护。版权所有。