کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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6269213 | 1295127 | 2012 | 13 صفحه PDF | دانلود رایگان |

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.
⺠Estimation of non-stationary Granger causal influences in noisy data. ⺠Time-resolved renormalized partial directed coherence using state space models. ⺠Dual Kalman filter and Expectation-Maximization algorithm used to reveal time-dependent interactions.
Journal: Journal of Neuroscience Methods - Volume 203, Issue 1, 15 January 2012, Pages 173-185