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.
Original languageEnglish
Number of pages288
Publication statusPublished - 24-Jul-2020
Event35th International Workshop
on Statistical Modelling
- Bilbao, Spain
Duration: 20-Jul-202024-Jul-2020
Conference number: 35


Conference35th International Workshop
on Statistical Modelling
Abbreviated titleIWSM 2020
Internet address

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