The newly developed Huber mean provides a more stable and reliable way to compute averages for data lying on curved geometric spaces, or Riemannian manifolds. By combining the strengths of ...

Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...

Divergence estimators have emerged as quintessential tools in statistical inference, particularly in contexts where traditional likelihood‐based methods fail under model misspecification or data ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Dr Max Welz introduces research aiming to make statistical analyses robust against so-called ‘contamination’ in rating data stemming from low-quality survey responses. Empirical research in the social ...

Kaitlyn Cook is a biostatistician working to develop robust statistical methods for infectious disease treatment and prevention trials. Her research draws on ideas from the missing data literature, ...

Faculty in the Statistics in Epidemiology Hub develop statistical methods to guide population-level research on cancer prevention, early detection, and real-world outcomes. Their work supports the ...