Publication
Title
The cellwise minimum covariance determinant estimator
Author
Abstract
The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information that we wish to preserve. We propose a cellwise robust version of the MCD method, called cellMCD. Its main building blocks are observed likelihood and a penalty term on the number of flagged cellwise outliers. It possesses good breakdown properties. We construct a fast algorithm for cellMCD based on concentration steps (C-steps) that always lower the objective. The method performs well in simulations with cellwise outliers, and has high finite-sample efficiency on clean data. It is illustrated on real data with visualizations of the results. Supplementary materials for this article are available online.
Language
English
Source (journal)
Journal of the American Statistical Association. - Washington, D.C.
Publication
Washington, D.C. : 2023
ISSN
0162-1459
DOI
10.1080/01621459.2023.2267777
Volume/pages
(2023) , p. 1-12
ISI
001108163800001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 27.02.2024
Last edited 07.03.2024
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