关于格兰杰因果连接性估计器的统计性能
On the statistical performance of Granger-causal connectivity estimators.
作者信息
Sameshima Koichi, Takahashi Daniel Y, Baccalá Luiz A
机构信息
Radiology & Oncology Department, Faculdade de Medicina, University of São Paulo, São Paulo, SP, 01246-903, Brazil.
Psychology Department, Neuroscience Institute, Princeton University, Princeton, NJ, USA.
出版信息
Brain Inform. 2015 Jun;2(2):119-133. doi: 10.1007/s40708-015-0015-1. Epub 2015 Apr 22.
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.
在本文中，我们通过对三个广泛使用的玩具模型进行全新的蒙特卡罗模拟，扩展了Sameshima等人（见：Slezak等人（编）《计算机科学讲义》，2014年）对线性连通性的统计检测性能评估。这些模拟针对经典时域多变量格兰杰因果检验、信息偏直接相干性、信息直接传递函数，在不同数据记录长度下进行，并且包括条件多变量格兰杰因果关系，其行为被发现是异常的。
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