Generative Conditional Missing Imputation Networks

• URL: http://arxiv.org/abs/2601.00517v1
• Date: Fri, 02 Jan 2026 00:39:12 GMT
• Title: Generative Conditional Missing Imputation Networks
• Authors: George Sun, Yi-Hui Zhou,
• Abstract summary: We introduce a sophisticated generative conditional strategy designed to impute missing values within datasets.<n>Specifically, we elucidate the theoretical underpinnings of the Generative Conditional Missing Imputation Networks (GCMI)<n>We enhance the robustness and accuracy of GCMI by integrating a multiple imputation framework using a chained equations approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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