Publication

Correction for the shrinkage effect in Gaussian graphical models

Bernal, V., Guryev, V., Bischoff, R., Horvatovich, P. & Grzegorczyk, M., 24-Jul-2020, p. 281-284. 4 p.

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
Pages281-284
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
https://wp.bcamath.org/iwsm2020/

Conference

Conference35th International Workshop
on Statistical Modelling
Abbreviated titleIWSM 2020
CountrySpain
CityBilbao
Period20/07/202024/07/2020
Internet address

Event

35th International Workshop
on Statistical Modelling

20/07/202024/07/2020

Bilbao, Spain

Event: Conference

ID: 130714012