Bridging classical, fuzzy, and approaches for missing data imputation: A unified comparative analysis
Year: 2026
Type: article
Abstract: Missing data remains a persistent challenge in data analysis, compromising statistical validity, predictive performance, and inferential robustness across diverse application domains. This paper presents a comprehensive comparative analysis of imputation methodologies, from foundational statistical approaches to state-of-the-art hybrid deep learning architectures. We establish a unified taxonomical organization that includes principal methodological families: classical statistical methods, machine learning and ensemble approaches, deep generative models, fuzzy logic systems, and hybrid architectures. Through rigorous empirical evaluation on benchmark datasets under varying missingness mechanisms—validated through convergent non-parametric statistical testing—we establish robust performance hierarchies and operational guidelines. Our findings demonstrate the statistical superiority of hybrid methodologies over both monolithic deep learning architectures and classical baselines, with hybrid approaches exhibiting enhanced precision and notable resilience to non-ignorable missingness. We further identify a structured Pareto frontier that balances computational efficiency and imputation accuracy, enabling context-specific method selection. Practical recommendations stratify methodological choices by operational context— critical applications that require maximal precision, real-time systems that demand computational efficiency, and scenarios characterized by complex missing mechanisms. This work provides theoretical foundations, empirical validation, and actionable decision frameworks for the rigorous handling of missing data in contemporary data science pipelines.
Institution: Sidi Mohamed Ben Abdellah University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7166369713