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

Background Despite anticoagulation, patients with atrial fibrillation (AF) experience persistent elevated cardiovascular risk ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in ...

Researchers have identified specific coupled patterns of brain activity and gene expression that help explain impulsive ...

Abstract: Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ...

ABSTRACT: This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and ...

Scientists harnessed a new method to precisely measure the amount of information the brain can store, and it could help advance our understanding of learning. When you purchase through links on our ...

Abstract: Correlation analysis is of great significance for exploring the multivariate data sets as it helps researchers toward an in-depth understanding of the complex interactions and relationships ...

ABSTRACT: Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and ...

In semiconductor manufacturing, especially in electrical test data, but also in other parameters, there are often sets of parameters that are very highly correlated. Even a change in the correlation ...