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
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;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.642