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