Estimating causal dependencies in networks of nonlinear stochastic dynamical systems

Linda Sommerlade, Michael Eichler, Michael Jachan, Kathrin Henschel, Jens Timmer, Bjoern Schelter

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The inference of causal interaction structures in multivariate systems enables a deeper understanding of the investigated network. Analyzing nonlinear systems using partial directed coherence requires high model orders of the underlying vector-autoregressive process. We present a method to overcome the drawbacks caused by the high model orders. We calculate the corresponding statistics and provide a significance level. The performance is illustrated by means of model systems and in an application to neurological data.

Original languageEnglish
Article number051128
Number of pages9
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Volume80
Issue number5
DOIs
Publication statusPublished - Nov 2009

Cite this

Estimating causal dependencies in networks of nonlinear stochastic dynamical systems. / Sommerlade, Linda; Eichler, Michael; Jachan, Michael; Henschel, Kathrin; Timmer, Jens; Schelter, Bjoern.

In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics, Vol. 80, No. 5, 051128, 11.2009.

Research output: Contribution to journalArticle

Sommerlade, Linda ; Eichler, Michael ; Jachan, Michael ; Henschel, Kathrin ; Timmer, Jens ; Schelter, Bjoern. / Estimating causal dependencies in networks of nonlinear stochastic dynamical systems. In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics. 2009 ; Vol. 80, No. 5.
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