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Mitigating the effects of measurement noise on Granger causality | | Hariharan Nalatore
; Govindan Rangarajan
; Mingzhou Ding
; | | Date: |
19 Nov 2007 | | Abstract: | 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 (EM) algorithm. Numerical examples are used to
demonstrate the effectiveness of the denoising approach. | | Source: | arXiv, 0711.2855 | | Services: | Forum | Review | PDF | Favorites | |
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