发表机构
Huazhong University of Science and Technology; Peking University Health Science Center; Beijing International Center for Mathematical Research, Peking University; Colorado State University（华中科技大学; 北京大学医学部; 北京国际数学研究中心，北京大学; 科罗拉多州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对变量含误差问题提出数据融合方法，通过条件可迁移性假设和重复测量识别误差分布，并开发结合反卷积与正交校正的估计器，实现异质性人群下的有效推断。
AI 中文摘要
我们研究变量含误差问题，其中目标研究仅包含一个未观测暴露变量的单一易错替代变量，而外部源研究则提供来自不同人群的重复替代测量。测量误差分布允许依赖于观测到的无误差变量，且无误差变量的分布本身在不同研究之间可能不同。我们引入一个条件可迁移性假设，使得在源-目标异质性下能够利用外部重复测量。结合额外的重复误差条件，该假设识别出目标条件测量误差分布。基于这一识别结果，我们为广泛的目标泛函类别开发了一个数据融合估计器。该估计器结合了条件反卷积、灵活扰动估计和正交校正，后者减少了对扰动估计的一阶敏感性。对于所提出的估计器，我们发展了一个统一的谱理论，涵盖扩散谱和有限原子谱目标泛函，推导了通用渐近展开，并建立了一致性和目标特定的收敛速率界。所得的收敛速率界联合依赖于测量误差、潜在暴露变量和目标泛函的谱性质。对于有限原子谱目标，我们进一步建立了联合高斯和自助法极限，从而在额外中心化条件下对光滑矩变换进行推断。在报告的模拟中，Fuse-EIV 对主要暴露相关系数具有较小的偏差。应用于国家健康与营养检查调查的数据说明了考虑人群异质性和误差异方差性如何能够改变实证结论。
英文摘要
We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate measurements from a different population. The measurement error distribution is allowed to depend on the observed error-free variables, and the error-free variable distribution itself may differ between studies. We introduce a conditional transportability assumption that enables the use of external repeated measurements under source-target heterogeneity. Together with additional replicate-error conditions, it identifies the target conditional measurement-error distribution. Building on this identification result, we develop a data-fusion estimator for a broad class of target functionals. The estimator combines conditional deconvolution, flexible nuisance estimation, and orthogonal correction that reduces first-order sensitivity to nuisance estimation. For the proposed estimator, we develop a unified spectral theory covering both diffuse-spectrum and finite atomic-spectrum target functionals, derive a general asymptotic expansion, and establish consistency and target-specific convergence-rate bounds. The resulting convergence-rate bounds depend jointly on the spectral properties of the measurement error, the latent exposure, and the target functional. For finite atomic-spectrum targets, we further establish joint Gaussian and bootstrap limits, yielding inference for smooth moment transformations under an additional centering condition. In the reported simulations, Fuse-EIV has small bias for the primary exposure-related coefficient. Applications to the National Health and Nutrition Examination Survey illustrate how accounting for population heterogeneity and error heteroscedasticity can change empirical conclusions.
