Missing data can derail even the most promising analysis, but modern imputation techniques are transforming this challenge into a solvable problem. From straightforward substitutions to advanced ...

Missing data imputation is a critical process in data analysis, enabling researchers to infer plausible values for absent observations. Over recent decades, a variety of methods have emerged, ranging ...

Researchers from the National Institute of Health Data Science at Peking University and the Department of Clinical Epidemiology and Biostatistics at Peking University People's Hospital have conducted ...