Challenges of cellwise outliers

Abstract

It is well-known that real data often contain outliers. The term outlier typically refers to a case, that is, a row of the $n\times d$ data matrix. In recent times a different type has come into focus, the cellwise outliers. These are suspicious cells (entries) that can occur anywhere in the data matrix. Even a relatively small proportion of outlying cells can contaminate over half the rows, which is a problem for rowwise robust methods. In this article we discuss the challenges posed by cellwise outliers, and some methods developed so far to deal with them. We obtain new results on cellwise breakdown values for location, covariance and regression. We also propose a cellwise robust method for correspondence analysis, with real data illustrations. The paper concludes by formulating some points for debate.

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Cited by in corpus (8)

• The Cellwise Minimum Covariance Determinant Estimator
• Robust discriminant analysis
• Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation
• Cellwise outlier detection in heterogeneous populations
• Comments on "Challenges of cellwise outliers" by Jakob Raymaekers and Peter J. Rousseeuw
• Correspondence analysis: handling cell-wise outliers via the reconstitution algorithm
• Robust Principal Components by Casewise and Cellwise Weighting
• Casewise and Cellwise Robust Multilinear Principal Component Analysis