Imputation methods provide essential statistical tools for addressing missing data, thereby minimising bias and enhancing the reliability of parameter estimates. In statistical estimation, missing ...

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 ...

A new review published in Artificial Intelligence and Autonomous Systems（AIAS） highlights how artificial intelligence can tackle the pervasive problem of missing traffic data in intelligent ...

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...