Cases where what’s been used up, leftover, missing or accumulated can give us valuable insights or help us make better decisions about health and safety, design, or policy changes.

A new review published in Artificial Intelligence and Autonomous Systems（AIAS） highlights how artificial intelligence can ...

Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too ...

Missing Data Analysis and Multiple Imputation Methods Publication Trend The graph below shows the total number of publications each year in Missing Data Analysis and Multiple Imputation Methods.

In observational studies, propensity score methods are popular for estimating causal effects. With completely observed data, this approach is valid under several assumptions; however, in practice data ...