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Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

机译:矢量自动评级建模与GRANGER因果关系应用中的数值限制分析脑电图时间序列

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In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
机译:在本章本章,研究了基于简单向量自回归(VAR)建模的GRANGER-因因果的潜在问题进行了研究到EEG数据。虽然一些初步研究测试了数据是否支持VAR的平稳假设,但估计模型的稳定性很少(如果有的话)已经过验证。事实上,在违反稳定条件的情况下,过程可能表现出类似的行为甚至爆炸性。问题由一个例子说明。

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