5 papers · 1 filter
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…
Outlier-Robust Multi-Group Gaussian Mixture Modeling with Flexible Group Reassignment
Patricia Puchhammer, Ines Wilms, Peter Filzmoser
Do expert-defined or diagnostically-labeled data groups align with clusters inferred through statistical modeling? If not, where do discrepancies between predefined labels and mode…
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…
Sparse outlier-robust PCA for multi-source data
Patricia Puchhammer, Ines Wilms, Peter Filzmoser
Sparse and outlier-robust Principal Component Analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single dataset whereas mul…
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…