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September 30, 2025Open Access

Cellwise and Casewise Robust Covariance in High Dimensions

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The cellRCov method effectively manages casewise and cellwise outliers, improving covariance estimates.
Simulation studies reveal that cellRCov outperforms existing methods in contaminated and missing data scenarios, offering better accuracy.
The method's framework utilizes robust PCA and ridge-type regularization, enhancing the stability of covariance matrix estimation.
Real-world application in anomaly detection demonstrates the practical utility of cellRCov and its derived methods.

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New robust covariance method improves analysis of outliers and missing data across dimensions, suggesting enhanced reliability.

Centofanti et al. (2025) studied this question.

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