On the statistical performance of Granger-causal connectivity estimators

In this article, we extend the statistical detection performance evaluation of linear connectivity from Sameshima et al. (in: Slezak et al. (eds.) Lecture Notes in Computer Science, 2014) via brand new Monte Carlo simulations of three widely used toy models under different data record lengths for a...

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Detalles Bibliográficos
Autores principales: Sameshima, Koichi, Takahashi, Daniel Y., Baccalá, Luiz A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883150/
https://www.ncbi.nlm.nih.gov/pubmed/27747486
http://dx.doi.org/10.1007/s40708-015-0015-1
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author Sameshima, Koichi
Takahashi, Daniel Y.
Baccalá, Luiz A.
author_facet Sameshima, Koichi
Takahashi, Daniel Y.
Baccalá, Luiz A.
author_sort Sameshima, Koichi
collection PubMed
description In this article, we extend the statistical detection performance evaluation of linear connectivity from Sameshima et al. (in: Slezak et al. (eds.) Lecture Notes in Computer Science, 2014) via brand new Monte Carlo simulations of three widely used toy models under different data record lengths for a classic time domain multivariate Granger causality test, information partial directed coherence, information directed transfer function, and include conditional multivariate Granger causality whose behaviour was found to be anomalous.
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spelling pubmed-48831502016-08-19 On the statistical performance of Granger-causal connectivity estimators Sameshima, Koichi Takahashi, Daniel Y. Baccalá, Luiz A. Brain Inform Article In this article, we extend the statistical detection performance evaluation of linear connectivity from Sameshima et al. (in: Slezak et al. (eds.) Lecture Notes in Computer Science, 2014) via brand new Monte Carlo simulations of three widely used toy models under different data record lengths for a classic time domain multivariate Granger causality test, information partial directed coherence, information directed transfer function, and include conditional multivariate Granger causality whose behaviour was found to be anomalous. Springer Berlin Heidelberg 2015-04-22 /pmc/articles/PMC4883150/ /pubmed/27747486 http://dx.doi.org/10.1007/s40708-015-0015-1 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Sameshima, Koichi
Takahashi, Daniel Y.
Baccalá, Luiz A.
On the statistical performance of Granger-causal connectivity estimators
title On the statistical performance of Granger-causal connectivity estimators
title_full On the statistical performance of Granger-causal connectivity estimators
title_fullStr On the statistical performance of Granger-causal connectivity estimators
title_full_unstemmed On the statistical performance of Granger-causal connectivity estimators
title_short On the statistical performance of Granger-causal connectivity estimators
title_sort on the statistical performance of granger-causal connectivity estimators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883150/
https://www.ncbi.nlm.nih.gov/pubmed/27747486
http://dx.doi.org/10.1007/s40708-015-0015-1
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