Statistical procedures for missing data have vastly improved, yet misconception and
unsound practice still abound. The authors frame the missing-data problem, review …

This review presents a practical summary of the missing data literature, including a sketch of
missing data theory and descriptions of normal-model multiple imputation (MI) and …

For scientific discoveries to be valid—whether in theory or empirically—a phenomenon must
be accurately described: The scientist must use appropriate counterfactuals and eliminate …

Missing data is common problem faced by researchers and data scientists. Therefore, it is
required to handle them appropriately in order to get better and accurate results of data …

Constructing multi-source satellite-derived water quality (WQ) products in inland and
nearshore coastal waters from the past, present, and future missions is a long-standing …

Nitrogen (N) is an important nutrient for crop growth. However, the overuse of N fertilizers
has led to a series of devastating global environmental issues. Recent studies show that …

We challenge a common assumption underlying most supervised deep learning: that a
model makes a prediction depending only on its parameters and the features of a single …

Although political violence has been perpetrated on behalf of a wide range of political
ideologies, it is unclear whether there are systematic differences between ideologies in the …

Treatment of missing data is usually included in a section of its own in most textbooks,
presenting best or most convenient practices, based on the methods presented and the …

Why does public conflict over societal risks persist in the face of compelling and widely
accessible scientific evidence? We conducted an experiment to probe two alternative …