TY - JOUR
T1 - Granger causality analysis with nonuniform sampling and its application to pulse-coupled nonlinear dynamics
AU - Zhang, Yaoyu
AU - Xiao, Yanyang
AU - Zhou, Douglas
AU - Cai, David
N1 - Funding Information:
This work is supported by NYU Abu Dhabi Institute G1301 (Y.Z., Y.X., D.Z., D.C.), NSFC Grant No. 91230202, Shanghai Rising-Star Program No. 15QA1402600 (D.Z.), NSF Grant No. DMS-1009575 (D.C.), Shanghai Grants No. 14JC1403800 and No. 15JC1400104, and SJTU-UM Collaborative Research Program (D.C., D.Z.).
Publisher Copyright:
© 2016 American Physical Society.
PY - 2016/4/26
Y1 - 2016/4/26
N2 - The Granger causality (GC) analysis is an effective approach to infer causal relations for time series. However, for data obtained by uniform sampling (i.e., with an equal sampling time interval), it is known that GC can yield unreliable causal inference due to aliasing if the sampling rate is not sufficiently high. To solve this unreliability issue, we consider the nonuniform sampling scheme as it can mitigate against aliasing. By developing an unbiased estimation of power spectral density of nonuniformly sampled time series, we establish a framework of spectrum-based nonparametric GC analysis. Applying this framework to a general class of pulse-coupled nonlinear networks and utilizing some particular spectral structure possessed by these nonlinear network data, we demonstrate that, for such nonlinear networks with nonuniformly sampled data, reliable GC inference can be achieved at a low nonuniform mean sampling rate at which the traditional uniform sampling GC may lead to spurious causal inference.
AB - The Granger causality (GC) analysis is an effective approach to infer causal relations for time series. However, for data obtained by uniform sampling (i.e., with an equal sampling time interval), it is known that GC can yield unreliable causal inference due to aliasing if the sampling rate is not sufficiently high. To solve this unreliability issue, we consider the nonuniform sampling scheme as it can mitigate against aliasing. By developing an unbiased estimation of power spectral density of nonuniformly sampled time series, we establish a framework of spectrum-based nonparametric GC analysis. Applying this framework to a general class of pulse-coupled nonlinear networks and utilizing some particular spectral structure possessed by these nonlinear network data, we demonstrate that, for such nonlinear networks with nonuniformly sampled data, reliable GC inference can be achieved at a low nonuniform mean sampling rate at which the traditional uniform sampling GC may lead to spurious causal inference.
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U2 - 10.1103/PhysRevE.93.042217
DO - 10.1103/PhysRevE.93.042217
M3 - Article
AN - SCOPUS:84964789473
VL - 93
JO - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
JF - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
SN - 1063-651X
IS - 4
M1 - 042217
ER -