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 ...

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 ...

In longitudinal clinical trials, missing data is a threat to scientific integrity. Whether due to patient dropouts, missed visits, or protocol deviations, these gaps can distort results, reduce ...

ABSTRACT A Bayesian uncertainty analysis of 12 parameters of the Bern2.5D climate model is presented. This includes an extensive sensitivity study with respect to the major statistical assumptions.

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, ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

Robust Engineering techniques developed by Taguchi have traditionally applied to the optimization of engineering designs. Robust Engineering methods also may be applied to software testing of ECU ...