## Samenvatting

Gaussian graphical models (GGMs) are network models where random

variables are represented by nodes and their pair-wise partial correlation by

edges. The inference of a GGM demands the estimation of the precision matrix

(i.e. the inverse of the covariance matrix); however, this becomes problematic

when the number of variables is larger than the sample size. Covariance estimators based on shrinkage (a type of regularization) overcome these pitfalls and result in a ’shrunk’ version of the GGM. Traditionally, shrinkage is justified at model level (as a regularized covariance). In this work, we re-interpret the shrinkage from a data level perspective (as a regularized data). Our result allows the propagation of uncertainty from the data into the GGM structure.

variables are represented by nodes and their pair-wise partial correlation by

edges. The inference of a GGM demands the estimation of the precision matrix

(i.e. the inverse of the covariance matrix); however, this becomes problematic

when the number of variables is larger than the sample size. Covariance estimators based on shrinkage (a type of regularization) overcome these pitfalls and result in a ’shrunk’ version of the GGM. Traditionally, shrinkage is justified at model level (as a regularized covariance). In this work, we re-interpret the shrinkage from a data level perspective (as a regularized data). Our result allows the propagation of uncertainty from the data into the GGM structure.

Originele taal-2 | English |
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Pagina's | 285 |

Aantal pagina's | 288 |

Status | Published - 24-jul-2020 |

Evenement | 35th International Workshop on Statistical Modelling - Bilbao, Spain Duur: 20-jul-2020 → 24-jul-2020 Congresnummer: 35 https://wp.bcamath.org/iwsm2020/ |

### Conference

Conference | 35th International Workshop on Statistical Modelling |
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Verkorte titel | IWSM 2020 |

Land | Spain |

Stad | Bilbao |

Periode | 20/07/2020 → 24/07/2020 |

Internet adres |