mlsanityData to 14 Oct 2026

Review for Handling Missing Data with special missing mechanism

arXiv 2404.04905 ↗First posted 7 Apr 2024stat.ME cs.AI cs.LGcited by 7

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Abstract (arXiv)

Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes. Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world. Three missing mechanisms are defined in the literature: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), each presenting unique challenges in imputation. Most existing work are focused on MCAR that is relatively easy to handle. The special missing mechanisms of MNAR and MAR are less explored and understood. This article reviews existing literature on handling missing values. It compares and contrasts existing methods in terms of their ability to handle different missing mechanisms and data types. It identifies research gap in the existing literature and lays out potential directions for future research in the field. The information in this review will help data analysts and researchers to adopt and promote good practices for handling missing data in real-world problems.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 26 of its 111 references that are in the map. First posted on arXiv 7 Apr 2024.

Builds on

26 of 111 references.

References · 26

  1. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  2. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  3. 2022Human Motion Diffusion Modelcited by 1,080
  4. 2019Modeling Tabular data using Conditional GANcited by 699
  5. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
  6. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 622
  7. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  8. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 447
  9. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  10. 2018Stochastic Adversarial Video Predictioncited by 173
  11. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 172
  12. 2020Handling Missing Data with Graph Representation Learningcited by 133
14 more
  1. 2020Missing Data Imputation using Optimal Transportcited by 100
  2. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  3. 2022Diffusion models for missing value imputation in tabular datacited by 77
  4. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 75
  5. 2019VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generationcited by 74
  6. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  7. 2020not-MIWAE: Deep Generative Modelling with Missing not at Random Datacited by 62
  8. 2018FloWaveNet : A Generative Flow for Raw Audiocited by 54
  9. 2018Processing of missing data by neural networkscited by 37
  10. 2020Conditional GAN for timeseries generationcited by 37
  11. 2018MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfercited by 33
  12. 2021Identifiable Generative Models for Missing Not at Random Data Imputationcited by 30
  13. 2018Learning Representations of Missing Data for Predicting Patient Outcomescited by 6
  14. 2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategiescited by 4
  2. 2024Reconstructing dynamics from sparse observations with no training on target systemcited by 2
  3. 2025Developing robust methods to handle missing data in real-world applications effectivelycited by 1
  4. 2026SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Datacited by 1
  5. 2024Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Taskscited by 0
  6. 2025Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learningcited by 0
  7. 2026Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoringcited by 0