提出生成式条件缺失值填补方法，提升数据完整性与分析可靠性。
Generative Conditional Missing Imputation Networks
- 基于生成模型构建条件填补框架，理论支持完整
- 融合链式方程多重填补，显著提升稳定性和准确率
- 在基准数据集上优于现有主流方法，适合高要求数据分析
本文提出一种生成式条件缺失值填补网络（GCMI），针对统计分析中缺失数据问题，系统阐述其在完全随机缺失（MCAR）和随机缺失（MAR）机制下的理论优势。通过引入链式方程的多重填补框架，进一步增强模型稳定性与填补精度。在多个基准数据集上的模拟与实证评估表明，所提方法在填补准确性与鲁棒性方面均显著优于当前主流技术，验证了其在实际数据处理中的有效性与前沿潜力。
原文摘要 · Abstract (English)
In this study, we introduce a sophisticated generative conditional strategy designed to impute missing values within datasets, an area of considerable importance in statistical analysis. Specifically, we initially elucidate the theoretical underpinnings of the Generative Conditional Missing Imputation Networks (GCMI), demonstrating its robust properties in the context of the Missing Completely at Random (MCAR) and the Missing at Random (MAR) mechanisms. Subsequently, we enhance the robustness and accuracy of GCMI by integrating a multiple imputation framework using a chained equations approach. This innovation serves to bolster model stability and improve imputation performance significantly. Finally, through a series of meticulous simulations and empirical assessments utilizing benchmark datasets, we establish the superior efficacy of our proposed methods when juxtaposed with other leading imputation techniques currently available. This comprehensive evaluation not only underscores the practicality of GCMI but also affirms its potential as a leading-edge tool in the field of statistical data analysis.
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