title: Kernel canonical-correlation Granger causality for multiple time series.
abstract: Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.
authors: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors: Guorong
authors: G
authors: Wu
authors: Xujun
authors: X
authors: Duan
authors: Wei
authors: W
authors: Liao
authors: Qing
authors: Q
authors: Gao
authors: Huafu
authors: H
authors: Chen