### 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 |
---|---|

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 |

## Fingerprint Dive into the research topics of 'Estimating partial correlations using the Oja sign covariance matrix'. Together they form a unique fingerprint.

## Cite this

Vogel, D., & Fried, R. (2008). Estimating partial correlations using the Oja sign covariance matrix. In P. Brito (Ed.),

*Compstat 2008: Proceedings in Computational Statistics. Vol. II. : Proceedings in Computational Statistics*(Vol. 2, pp. 721-729). Physica-Verlag HD.