1 citations · 1 across the 3 of their papers we have counts for
11 papers
Cellwise and Casewise Robust Covariance in High Dimensions
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a diffe…
Cellwise Robust Discriminant Analysis
Fabio Centofanti, Can Hakan Dagidir, Mia Hubert +1
Classical discriminant analysis (DA) is based on the mean and empirical covariance matrix of each class, both of which are sensitive to outliers in the data. In the past the focus…
Cellwise and Casewise Robust Multivariate Regression with Inference
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
Multivariate linear regression is a fundamental statistical task, but classical estimators such as ordinary least squares are highly sensitive to outliers. These may occur as casew…
Robust Tensor-on-Tensor Regression
Mehdi Hirari, Fabio Centofanti, Mia Hubert +1
Tensor-on-tensor (TOT) regression is an important tool for the analysis of tensor data, aiming to predict a set of response tensors from a corresponding set of predictor tensors. H…
Casewise and Cellwise Robust Multilinear Principal Component Analysis
Mehdi Hirari, Fabio Centofanti, Mia Hubert +1
Multilinear Principal Component Analysis (MPCA) is an important tool for analyzing tensor data. It performs dimension reduction similar to PCA for multivariate data. However, stand…
Robust Principal Components by Casewise and Cellwise Weighting
Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw
Principal component analysis (PCA) is a fundamental tool for analyzing multivariate data. Here the focus is on dimension reduction to the principal subspace, characterized by its p…