arXiv：cs.LG· Huali Zhao, Molei Liu, Tianying Wang·· 4 小时前AI 评分29
Data Fusion for Errors-in-Variables：基于条件可迁移性的数据融合估计
Data Fusion for Errors-in-Variables
AI 导读
研究针对目标研究仅含单个易错替代变量、外部源研究提供重复替代测量的 errors-in-variables 问题，提出条件可迁移性假设与复制误差条件，识别目标条件测量误差分布。
正文
Abstract: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.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME) |
| Cite as: | arXiv:2610.07048 [stat.ML] |
| (or arXiv:2610.07048v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07048 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianying Wang [view email]
[v1]
Mon, 5 Oct 2026 02:22:43 UTC (119 KB)
来源：arXiv：cs.LG · arxiv.org