title: Estimating causal dependencies in networks of nonlinear stochastic dynamical systems.
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.
authors.affiliation: Department of Physics, University of Freiburg, Hermann-Herder-Str. 3, 79104 Freiburg, Germany. linda.sommerlade@fdm.uni-freiburg.de
authors.forename: Linda
authors.initials: L
authors.lastname: Sommerlade
authors.forename: Michael
authors.initials: M
authors.lastname: Eichler
authors.forename: Michael
authors.initials: M
authors.lastname: Jachan
authors.forename: Kathrin
authors.initials: K
authors.lastname: Henschel
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013269:Stochastic Processes