Abstract
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 language | English |
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Title of host publication | Compstat 2008: Proceedings in Computational Statistics. Vol. II. |
Subtitle of host publication | Proceedings in Computational Statistics |
Editors | Paula Brito |
Publisher | Physica-Verlag HD |
Pages | 721-729 |
Number of pages | 9 |
Volume | 2 |
Publication status | Published - 2008 |