Background Missing data is a pervasive problem in longitudinal data analysis. Several
single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to …

Remotely sensed measurements and other machine learning predictions are increasingly
used in place of direct observations in empirical analyses. Errors in such measures may bias …

Purpose To evaluate the association of estimated plasma volume (ePV) and plasma volume
status (PVS) on admission with the outcomes in COVID-19-related acute respiratory distress …

In this study, the ability of numerous statistical and machine learning models to impute water
quality data was investigated at three monitoring stations along the Langat River in …

Objective Children with special health care needs (SHCN) due to a chronic health condition
perform more poorly at school compared to their classmates. There is still little knowledge on …

Using secondary data has many advantages, but there are also many limitations, including
the lack of relevant information. This article draws on a previous study that used secondary …

Incomplete longitudinal data are common in life-course epidemiology and may induce bias
leading to incorrect inference. Multiple imputation (MI) is increasingly preferred for handling …

Advances in digital technology have greatly increased the ease of collecting intensive
longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of …

This thesis addresses the challenge of dealing with missing data, which is an inevitable
issue in quantitative studies. The appropriate treatment of missing data is complex and can …

The world research community have led data science to the level that it can mimic human
capabilities very well. Prominent research is going on in deep learning areas such as …