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On the probability of (falsely) connecting two distinct components when learning a GGM.
- Source :
-
Communications in Statistics: Theory & Methods . 2024, Vol. 53 Issue 11, p4107-4115. 9p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NEIGHBORS
Subjects
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 53
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 176582864
- Full Text :
- https://doi.org/10.1080/03610926.2023.2173973