Correction for the shrinkage effect in Gaussian graphical models

Research output: Contribution to conferencePaperAcademic


Gaussian graphical models (GGMs) are probabilistic graphical models
based on partial correlation. A GGM consists of a network of nodes (representing
the random variables) connected by edges (their partial correlation). To infer a
GGM, the inverse of the covariance matrix (the precision matrix) is required. The
main challenge is that when the number of variables is larger than the sample size,
the (sample) covariance is ill conditioned (or not invertible). Shrinkage methods
consist in regularizing the estimator of the covariance matrix to make it invertible
(and well conditioned); however, the effect of the shrinkage on the final network
topology has not been studied so far.
Original languageEnglish
Number of pages4
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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