title: On the statistical performance of Granger-causal connectivity estimators.
abstract: 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.
authors.affiliation: Radiology & Oncology Department, Faculdade de Medicina, University of São Paulo, São Paulo, SP, 01246-903, Brazil. ksameshi@usp.br.
authors.forename: Koichi
authors.initials: K
authors.lastname: Sameshima
authors.affiliation: Psychology Department, Neuroscience Institute, Princeton University, Princeton, NJ, USA.
authors.forename: Daniel Y
authors.initials: DY
authors.lastname: Takahashi
authors.affiliation: Department of Telecommunications and Control, Escola Politécnica, University of São Paulo, São Paulo, SP, Brazil.
authors.forename: Luiz A
authors.initials: LA
authors.lastname: Baccalá
keywords: Conditional multivariate Granger causality; Directed transfer function; Granger causality; Null hypothesis test performance; Partial directed coherence