Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

We demonstrate the feasibility of constructing, interpreting and fitting computable log-linear models to categorical survey data with arbitrary non-nested patterns of non-ignorable non-response. Under ...

In the modern field of deep learning, linear attention mechanisms are gradually becoming a powerful tool for handling long sequence data. Recent research has revealed how these mechanisms 'decay' ...

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 ...

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 ...

Just as machine learning, artificial intelligence, data modeling and analytics platforms have transformed manufacturing, drug discovery, health care and operations in a host of other industries, these ...

A Covid-19 restrictions sign hangs outside a supermarket in Austin, Texas. Lauren Ancel Meyers at the University of Texas at Austin has shared her team’s modeling results with city officials who make ...