24 citations · 28 across the 5 of their papers we have counts for
5 papers
Testing Covariance Separability in High Dimensions
Tomas Masak, Marcus Mayrhofer, Una Radojičić
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…
Explainable Outlier Detection for Multivariate Functional Data
Marcus Mayrhofer, Una Radojičić, 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…
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…
Robust covariance estimation and explainable outlier detection for matrix-valued data
Marcus Mayrhofer, Una Radojičić, 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…
Multivariate outlier explanations using Shapley values and Mahalanobis distances
Marcus Mayrhofer, Peter Filzmoser
For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions origi…