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.
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Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.220-229, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_21〉
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Sergiy Pereverzyev Jr., Kateřina Hlaváčková-Schindler. Graphical Lasso Granger Method with 2-Levels-Thresholding for Recovering Causality Networks. Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.220-229, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_21〉. 〈hal-01286461〉

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