This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

We introduce the spherically projected multivariate linear model for directional data. This model treats directional observations as projections onto the unit sphere of unobserved responses from a ...

Responding to an impending hazard means that time is limited, so analysis and decision-making must proceed on an accelerated timetable. Modeling, numerical simulation, leading to predictive capacity, ...

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...

Kenya’s food markets are known for extreme volatility influenced by weather shocks, inflation, currency fluctuations, and ...

Researchers in Sweden have developed a method to use LiDAR data to enable more precise tilt and azimuth modeling of solar PV and solar thermal capacity at a regional or substation level. The model ...