Abstract
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.
| Original language | English |
|---|---|
| Pages | 285 |
| Number of pages | 288 |
| Publication status | Published - 24-Jul-2020 |
| Event | 35th International Workshop on Statistical Modelling - Bilbao, Spain Duration: 20-Jul-2020 → 24-Jul-2020 Conference number: 35 https://wp.bcamath.org/iwsm2020/ |
Conference
| Conference | 35th International Workshop on Statistical Modelling |
|---|---|
| Abbreviated title | IWSM 2020 |
| Country/Territory | Spain |
| City | Bilbao |
| Period | 20/07/2020 → 24/07/2020 |
| Internet address |