April 12, 2007 Robust statistical analysis methods capable of dealing with large complex data sets are required more than ever before in almost all branches of science. The European Science Foundation ...
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 ...
Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...
Divergence estimators have emerged as quintessential tools in statistical inference, particularly in contexts where traditional likelihood‐based methods fail under model misspecification or data ...
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 ...
A phase II, multi-center study of a new non-cytotoxic A3 adenosine receptor agonist CF101, dose-finding (randomized blinded) in patients (pts)with refractory metastatic colorectal cancer This is an ...
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 ...
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 ...
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