发表机构
University of Alberta; University of Western Ontario; Memorial Sloan Kettering Cancer Center; University of Prince Edward Island（阿尔伯塔大学; 西安大略大学; 纪念斯隆凯特琳癌症中心; 爱德华王子岛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对缺失数据、测量误差与异质性共存问题，提出整合树路由变分自编码器等的深度隐变量框架，模拟研究显示其性能优于现有深度生成插补方法。
AI 中文摘要
缺失数据、测量误差与总体异质性是分析现代观察性研究及机器学习应用数据时普遍存在的挑战。尽管这些问题常共存且相互作用，但现有研究多将其分开处理。我们提出一种统一概率框架，利用深度隐变量表示联合解决这些问题。该方法整合了新型分层树路由变分自编码器，具备模式感知隐表示与基于校准的去噪能力。此框架可处理包括MCAR（完全随机缺失）、MAR（随机缺失）、MNAR（非随机缺失）在内的缺失机制，同时学习子群特异性与全局共享的隐结构。引入的重聚路由机制使相关亚群间可选择性共享参数，兼具灵活性与更高统计效率。模拟研究显示，在复杂异质性缺失与测量误差场景下，该方法较现有深度生成插补方法有显著提升，为从现代医疗及其他高维应用的含噪不完整数据中学习提供了原则性方法。
英文摘要
Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arising from modern observational studies and machine learning applications. Although these problems frequently coexist and interact, they are often treated separately in existing works. We propose a unified probabilistic framework that jointly addresses these issues utilizing deep latent variable representation. The proposed method integrates a novel hierarchical tree-routed variational autoencoder with pattern-aware latent representations and calibration-based denoising. The framework accommodates missing data mechanisms, including MCAR, MAR, and MNAR, while simultaneously learning subgroup-specific and globally shared latent structure. The introduced reconvergent routing mechanism enables selective parameters to be shared across related subpopulations, which offers flexibility as well as improved statistical efficiency. Simulation studies demonstrate substantial improvements over existing deep generative imputation approaches under complex heterogeneous missingness and measurement-error settings. The proposed framework provides a principled approach for learning from noisy and incomplete data in modern healthcare and other high-dimensional applications.
Comments24 pages, 4 figures
