A research team from the Hong Kong University of Science and Technology (HKUST) has developed a novel AI-powered tool named ...

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 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 ...

Missing data present a perennial challenge in scientific research, potentially undermining the validity of conclusions if not addressed rigorously. The analysis of missing data encompasses a broad ...

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 the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; ...

This is a preview. Log in through your library . Abstract Model averaging, specifically information theoretic approaches based on Akaike's information criterion (IT-AIC approaches), has had a major ...

Haewon Jeong, an assistant professor in UC Santa Barbara’s Electrical and Computer Engineering (ECE) Department, experienced a pivotal moment in her academic career when she was a postdoctoral fellow ...

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 ...