Scalable robust covariance and correlation estimates for data mining
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The complexity of computing the MCD-estimator
Theoretical Computer Science
A relaxed approach to combinatorial problems in robustness and diagnostics
Statistics and Computing
Jump robust daily covariance estimation by disentangling variance and correlation components
Computational Statistics & Data Analysis
Testing noisy numerical data for monotonic association
Information Sciences: an International Journal
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The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent and asymptotically efficient at the normal distribution. The correlation matrix obtained from pairwise Gaussian rank correlations is always positive semidefinite, and very easy to compute, also in high dimensions. We compare the properties of the Gaussian rank correlation with the popular Kendall and Spearman correlation measures. A simulation study confirms the good efficiency and robustness properties of the Gaussian rank correlation. In the empirical application, we show how it can be used for multivariate outlier detection based on robust principal component analysis.