This cluster of papers focuses on the detection, impact, and handling of multicollinearity in regression analysis. It discusses methods for identifying outliers, robust estimation techniques, variance inflation factors, and the use of depth functions in analyzing data. The cluster also explores the relative importance of predictors, principal component analysis, and the application of these concepts to functional data.
Multicollinearity; Regression Analysis; Outlier Detection; Robust Estimation; Variance Inflation Factor; Principal Component Analysis; Relative Importance; Depth Functions; Robust Statistics; Functional Data