Your browser doesn't support javascript.
loading
Inference of Granger causal time-dependent influences in noisy multivariate time series.
Sommerlade, Linda; Thiel, Marco; Platt, Bettina; Plano, Andrea; Riedel, Gernot; Grebogi, Celso; Timmer, Jens; Schelter, Björn.
Affiliation
  • Sommerlade L; Department of Physics, University of Freiburg, Hermann-Herder-Str. 3, 79104 Freiburg, Germany. linda.sommerlade@fdm.uni-freiburg.de
J Neurosci Methods ; 203(1): 173-85, 2012 Jan 15.
Article in En | MEDLINE | ID: mdl-21944999
Inferring Granger-causal interactions between processes promises deeper insights into mechanisms underlying network phenomena, e.g. in the neurosciences where the level of connectivity in neural networks is of particular interest. Renormalized partial directed coherence has been introduced as a means to investigate Granger causality in such multivariate systems. A major challenge in estimating respective coherences is a reliable parameter estimation of vector autoregressive processes. We discuss two shortcomings typical in relevant applications, i.e. non-stationarity of the processes generating the time series and contamination with observational noise. To overcome both, we present a new approach by combining renormalized partial directed coherence with state space modeling. A numerical efficient way to perform both the estimation as well as the statistical inference will be presented.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Models, Neurological / Models, Theoretical / Nerve Net Limits: Animals Language: En Journal: J Neurosci Methods Year: 2012 Document type: Article Affiliation country: Germany Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Models, Neurological / Models, Theoretical / Nerve Net Limits: Animals Language: En Journal: J Neurosci Methods Year: 2012 Document type: Article Affiliation country: Germany Country of publication: Netherlands