A Deep Latent Variable Framework for Jointly Modeling Missingness, Measurement Error, and Heterogeneity
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 s...
| Authors | Yasin Khadem Charvadeh, Grace Y. Yi, Mithat Gönen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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