Estimating partial correlations using the Oja sign covariance matrix

Daniel Vogel, Roland Fried

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


We investigate the Oja sign covariance matrix (Oja SCM) for estimating partial correlations in multivariate data. The Oja SCM estimates directly a multiple of the precision matrix and is based on the concept of Oja signs, a multivariate generalisation of the univariate sign function, which obey some form of affine equivariance property. Simulations show that the asymptotic distribution gives a good approximation of the exact finite-sample distribution already for samples of moderate size. We find it to outperform the classical sample partial correlation in case of heavy-tailed distributions. The high computational costs are its main disadvantage.
Original languageEnglish
Title of host publicationCompstat 2008: Proceedings in Computational Statistics. Vol. II.
Subtitle of host publicationProceedings in Computational Statistics
EditorsPaula Brito
PublisherPhysica-Verlag HD
Number of pages9
Publication statusPublished - 2008


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