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.AP2025
A New View to Mission Profiles
Horst Lewitschnig, Marcus Mayrhofer, Peter Filzmoser
Mission profiles cover the conditions that a component, e.g., an electronic component of a vehicle, is exposed to during its lifecycle. Currently, these profiles typically provide…