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 ...

This is a preview. Log in through your library . Abstract Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey ...

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 ...

Background: Environmental and biomedical researchers frequently encounter laboratory data constrained by a lower limit of detection (LOD). Commonly used methods to address these leftcensored data, ...

This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is ...

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 ...