Inference of Granger causal time-dependent influences in noisy multivariate time series
2012 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 203, no 1, 173-185 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Elsevier , 2012. Vol. 203, no 1, 173-185 p.
Non-stationary causal influences; Time-resolved partial directed coherence; Vector autoregressive processes; State space models; Expectation-Maximization algorithm
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-73304DOI: 10.1016/j.jneumeth.2011.08.042ISI: 297896100022OAI: oai:DiVA.org:liu-73304DiVA: diva2:472129