HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Graphical Lasso Granger Method with 2-Levels-Thresholding for Recovering Causality Networks

Abstract : The recovery of the causality networks with a number of variables is an important problem that arises in various scientific contexts. For detecting the causal relationships in the network with a big number of variables, the so called Graphical Lasso Granger (GLG) method was proposed. It is widely believed that the GLG-method tends to overselect causal relationships. In this paper, we propose a thresholding strategy for the GLG-method, which we call 2-levels-thresholding, and we show that with this strategy the variable overselection of the GLG-method may be overcomed. Moreover, we demonstrate that the GLG-method with the proposed thresholding strategy may become superior to other methods that were proposed for the recovery of the causality networks.
Document type :
Conference papers
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01286461
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, March 10, 2016 - 5:59:43 PM
Last modification on : Thursday, March 31, 2022 - 1:00:19 PM
Long-term archiving on: : Sunday, November 13, 2016 - 3:29:55 PM

File

978-3-662-45504-3_21_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Sergiy Pereverzyev Jr., Kateřina Hlaváčková-Schindler. Graphical Lasso Granger Method with 2-Levels-Thresholding for Recovering Causality Networks. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. pp.220-229, ⟨10.1007/978-3-662-45504-3_21⟩. ⟨hal-01286461⟩

Share

Metrics

Record views

57

Files downloads

151