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...

Descripción completa

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
Descripción
Sumario: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.

Ejemplares similares