### Abstract

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 |

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### Cite this

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

**Estimating partial correlations using the Oja sign covariance matrix.** / Vogel, Daniel; Fried, Roland .

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

}

TY - GEN

T1 - Estimating partial correlations using the Oja sign covariance matrix

AU - Vogel, Daniel

AU - Fried, Roland

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - 2

SP - 721

EP - 729

BT - Compstat 2008: Proceedings in Computational Statistics. Vol. II.

A2 - Brito, Paula

PB - Physica-Verlag HD

ER -