On the probability of (falsely) connecting two distinct components when learning a GGM

Daniela De Canditiis and Marika Turdó

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 11, 4107-4115

Abstract: In this paper, we extend the result on the probability of (falsely) connecting two distinct components when learning a GGM (Gaussian Graphical Model) by the joint regression based technique. While the classical method of regression based technique learns the neighbours of each node one at a time through a Lasso penalized regression, its joint modification, considered here, learns the neighbours of each node simultaneously through a group Lasso penalized regression.

Date: 2024
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DOI: 10.1080/03610926.2023.2173973

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