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 rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too ...

Data is almost always incomplete. Patients drop out of clinical trials and survey respondents skip questions; schools fail to report scores, and governments ignore elements of their economies. When ...

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 set of k≥1 values (the ...

In longitudinal clinical trials, missing data is a threat to scientific integrity. Whether due to patient dropouts, missed visits, or protocol deviations, these gaps can distort results, reduce ...

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 ...

When it comes to economic assessment, feelings are no substitute for hard data. A plurality of Americans say that we’re in a recession; the actual numbers on jobs and gross domestic product show an ...