Title: The Gaussian rank correlation estimator: Robustness properties
Authors: Boudt, Kris ×
Cornelissen, Jonathan
Croux, Christophe #
Issue Date: 2012
Publisher: Kluwer Academic Publishers
Series Title: Statistics and Computing vol:22 issue:2 pages:471-483
Abstract: 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.
ISSN: 0960-3174
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
Research Center Finance, Leuven
Leuven Statistics Research Centre (LStat)
Faculty of Economics and Business (FEB) - miscellaneous
Department of Financial Management, Campus Carolus Antwerp
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
thegaussianrank.pdf Published 609KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science