Predictive mean matching (PMM) is a standard technique for the imputation of incomplete
continuous data. PMM imputes an actual observed value, whose predicted value is among a …

It is important to evaluate risk of bias of the primary studies included in systematic reviews
and meta-analyses. Since tools pertinent to surveys are scarce, the goal of the current …

A new methodology, imputation by feature importance (IBFI), is studied that can be applied
to any machine learning method to efficiently fill in any missing or irregularly sampled data. It …

Imputation of missing sensor-collected data is often an important step prior to machine
learning and statistical data analysis. One particular data imputation challenge is filling large …

Various job demands continuously threaten Emergency Medical Service (EMS) first
responders' safety and wellbeing. Drawing on Job Demands–Resources Theory, the …

Objectives Previous research has shown that while missing data are common in
bioarchaeological studies, they are seldom handled using statistically rigorous methods …

(1) Background: A previous study has shown that cognitive training with neurofeedback (CT-
NF) using down-regulation improves cognitive functions in young adults. Neurofeedback …

The objective of this study was to develop a novel copula-based time series (CTS) model to
forecast COVID-19 cases and trends based on wastewater SARS-CoV-2 viral load and …

Background Health games provide opportunities for the treatment and prevention of
childhood obesity. We developed a motion-controlled serious game for children that …

Hibernation is an adaptation to survive periods of stress, from food limitation or harsh
thermal conditions. A key question in contemporary ecology is whether rare, range-restricted …