Fast Robust Location and Scatter Estimation: A Depth-based Method

• Published in: Technometrics Vol. 66; no. 1; pp. 14 - 27
• Main Authors: Zhang, Maoyu, Song, Yan, Dai, Wenlin
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.01.2024; American Society for Quality
• Subjects: Algorithms; Asymptotic methods; Computational efficiency; Covariance; Data analysis; Discriminant analysis; Empirical analysis; Estimating techniques; Estimators; High-dimensional data; Multivariate analysis; Outliers; Outliers (statistics); Principal components analysis; Robustness; Robustness (mathematics); Simulation; Statistical analysis; Statistical depth
• ISSN: 0040-1706; 1537-2723
• DOI: 10.1080/00401706.2023.2216246

The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis, the critical step of which is to select a subset of a given size with the lowest sample covariance determinant. The concentration step (C-step) is a common tool for subset-seeking; however, it becomes computationally demanding for high-dimensional data. To alleviate the challenge, we propose a depth-based algorithm, termed as FDB , which replaces the optimal subset with the trimmed region induced by statistical depth. We show that the depth-based region is consistent with the MCD-based subset under a specific class of depth notions, for instance, the projection depth. With the two suggested depths, the FDB estimator is not only computationally more efficient but also reaches the same level of robustness as the MCD estimator. Extensive simulation studies are conducted to assess the empirical performance of our estimators. We also validate the computational efficiency and robustness of our estimators under several typical tasks such as principal component analysis, linear discriminant analysis, image denoise and outlier detection on real-life datasets. An R package FDB and additional results are available in the supplementary materials .