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 ...

In longitudinal clinical trials, missing data is a threat to scientific integrity. Whether due to patient dropouts, missed visits, or protocol deviations, these gaps can distort results, reduce ...

Scientists in China have developed a novel missingness-aware power forecasting method that leverages signal decomposition, multi-scale covariate interaction, and multi-domain collaborative transfer ...