3 papers
stat.ME2026
Testing Covariance Separability in High Dimensions
Tomas Masak, Marcus Mayrhofer, Una RadojiÄiÄ
Separability is an important structural assumption often placed on the covariance when working with matrix-variate data, because it greatly simplifies both interpretation and compu…
stat.ME2026
Explainable Outlier Detection for Multivariate Functional Data
Marcus Mayrhofer, Una RadojiÄiÄ, Horst Lewitschnig +1
This work addresses the challenges of robust covariance estimation and interpretable outlier detection for multivariate functional data with separable covariance structure. We deve…
stat.ME2026
Robust functional PCA for relative data
Jeremy Oguamalam, Peter Filzmoser, Karel Hron +2
This paper introduces a robust approach to functional principal component analysis (FPCA) for relative data, particularly density functions. While recent papers have studied densit…