A general statistical framework for assessing Granger causality
Sanggyun Kim,Emery N. Brown +1 more
- 14 Mar 2010
- pp 2222-2225
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TL;DR: In this article, the authors proposed a general statistical framework for assessing the causal interactions between non-Gaussian time series and continuous-valued time series, based on the relative reduction of the likelihood of x 1 by the exclusion of x 2 compared to the likelihood obtained using all the time series.
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Abstract: Assessing the causal relationship among multivariate time series is a crucial problem in many fields Granger causality has been widely used to identify the causal interactions between continuous-valued time series based on multivariate autoregressive models in the Gaussian case In order to extend the application of the Granger causality concept to non-Gaussian time series, we propose a general statistical framework for assessing the causal interactions In this study, the Granger causality from a time series x 2 to a time series x 1 is assessed based on the relative reduction of the likelihood of x 1 by the exclusion of x 2 compared to the likelihood obtained using all the time series Simulation results indicated that the proposed algorithm accurately predicted nature of interactions between discrete-valued time series as well as between continuousvalued time series
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Citations
The Relation between Granger Causality and Directed Information Theory: A Review
TL;DR: The links are developed by studying how measures based on directed information theory naturally emerge from Granger causality inference frameworks as hypothesis testing, and showing that the useful decomposition is blurred by instantaneous coupling.
Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media
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Assessing functional connectivity of neural ensembles using directed information.
TL;DR: Using physiologically realistic simulations, it is demonstrated that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation.
51
Incremental mutual information: a new method for characterizing the strength and dynamics of connections in neuronal circuits.
Abhinav Singh,Nicholas A. Lesica +1 more
TL;DR: A new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI), which improves on standard correlation-based measures and has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources.
Towards scalable critical alert mining
Bo Zong,Yinghui Wu,Jie Song,Ambuj K. Singh,Hasan Cam,Jiawei Han,Xifeng Yan +6 more
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TL;DR: An approximation algorithm is developed that obtains a near-optimal alert set in quadratic time, and pruning techniques are proposed to improve its runtime performance, and a faster approximation exists, when the alerts follow certain causal structure.
18
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