Nalatore, Hariharan and Ding, Mingzhou and Rangarajan, Govindan (2007) Mitigating the effects of measurement noise on Granger causality. In: Physical Review E, 75 (Part 1). 031123:1-10.
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Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination, (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to American Physical Society.|
|Department/Centre:||Division of Physical & Mathematical Sciences > Mathematics|
|Date Deposited:||22 Jun 2007|
|Last Modified:||19 Sep 2010 04:38|
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