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 ...

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

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 ...

Timothy E. Smetek, Kenneth W. Bauer Jr. Hyperspectral anomaly detection is a useful means for using hyperspectral imagery to locate unusual objects. Current anomaly detection methods commonly use ...

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 ...

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 ...

Chongzhi Di develops statistical methods for analyzing functional and longitudinal data in epidemiologic studies of physical activity and sedentary behavior, particularly using data from mobile-health ...

Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.

A new robust statistical method resists outliers, improving data reliability in AI, robotics, and medical imaging BUSAN, South Korea, Nov. 13, 2025 /PRNewswire/ -- In many modern sciences, data often ...