DocumentCode :
2481766
Title :
A Test of Granger Non-causality Based on Nonparametric Conditional Independence
Author :
Seth, Sohan ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
2620
Lastpage :
2623
Abstract :
In this paper we describe a test of Granger non-causality from the perspective of a new measure of nonparametric conditional independence. We apply the proposed test on two synthetic nonlinear problems where linear Granger causality fails and show that the proposed method is able to derive the true causal connectivity effectively.
Keywords :
stochastic processes; granger noncausality; nonparametric conditional independence; synthetic nonlinear problems; Accuracy; Biological system modeling; Couplings; Distribution functions; Kernel; Time series analysis; Yttrium; Granger causality; conditional independence; kernel methods; regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.642
Filename :
5595993
Link To Document :
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