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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.ME2024
Robust covariance estimation and explainable outlier detection for matrix-valued data
Marcus Mayrhofer, Una RadojiÄiÄ, Peter Filzmoser
This work introduces the Matrix Minimum Covariance Determinant (MMCD) method, a novel robust location and covariance estimation procedure designed for data that are naturally repre…