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 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 ...

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...

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 ...

We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the ...

I recently received the following question on data science methods from an avid reader of insideAI News who hails from Taiwan. I think the topics are very relevant to many folks in our audience so I ...