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 ...

When conducting surveys, two kinds of nonresponse may cause incomplete data files: unit nonresponse (complete nonresponse) and item nonresponse (partial nonresponse). The selectivity of the unit ...

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...

Missing data can plague researchers in many scenarios, arising from incomplete surveys, experimental objects broken or destroyed, or data collection/computational errors. This short course will ...

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS ...

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