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Handling the uncertainty issue of missingness via a mixture-structure-based method
1School of Data Science, Fudan University, Shanghai, 200433, China.
The International Journal of Biostatistics
|May 27, 2026
Summary
Uncertainty in missing data structures, especially missing not at random (MNAR) data, is addressed by a novel two-step mixture method. This approach unifies handling uncertainty and inference within an EM-based framework for robust results.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- Missing data are prevalent in real-world studies, posing challenges due to unknown missingness structures.
- Uncertainty arises from both the missing mechanism and the functional form of the missing data model.
- Missing not at random (MNAR) data presents particular difficulties for reliable statistical inference.
Purpose of the Study:
- To systematically examine sources of uncertainty in missing data, focusing on MNAR data.
- To propose a unified method for handling missing data uncertainty and conducting inference.
- To establish an identification framework for finite mixture models with MNAR data.
Main Methods:
- A two-step mixture-structure-based method is proposed, incorporating a model filtering pre-screening step.
- An expectation-maximization (EM)-based framework unifies uncertainty handling and inference.
- A two-layer postulated mixture is constructed to enhance flexibility and robustness.
Main Results:
- The method effectively handles uncertainty from both missing mechanisms and model forms.
- An identification framework is established for finite mixture models under MNAR.
- Demonstrated performance through simulation studies and application to the Medical Expenditure Panel Survey (MEPS).
Conclusions:
- The proposed method offers a robust approach to statistical inference with complex missing data structures.
- It addresses key uncertainties in missing data analysis, particularly for MNAR data.
- The unified framework simplifies the process of handling missing data and drawing reliable conclusions.
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