Relationship Between Granger Noncausality and Network Graph of State-Space Representations

被引：7

作者：

Jozsa, Monika ^{[1
,2
]}

Petreczky, Mihaly ^{[3
]}

Camlibel, M. Kanat ^{[2
]}

机构：

[1] Univ Lille, IMT Lille Douai, Unite Rech Informat Automat, F-59000 Lille, France

[2] Univ Groningen, Johann Bernoulli Inst Math & Comp Sci, NL-9700 AK Groningen, Netherlands

[3] Univ Lille, Ctr Rech Informat Signal & Automat Lille, CNRS, UMR 9189,Cent Lille,CRIStAL, F-59000 Lille, France

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2019年 / 64卷 / 03期

关键词：

Interconnected systems; stochastic systems; system realization; MODEL-REDUCTION; TIME-SERIES; BAYES NETS; CAUSALITY; IDENTIFICATION; REALIZATIONS;

D O I：

10.1109/TAC.2018.2832469

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The goal of this paper is to explore the relationship between the network graph of a state-space representation of an observed process and the causal relations among the components of that process. We will show that the existence of a linear time-invariant state-space representation, with its network graph being the star graph, is equivalent to (conditional) Granger noncausal relations among the components of the output process. Granger non-causality is a statistical concept, which applies to arbitrary processes and does not depend on the representation of the process. That is, we relate intrinsic properties of a process with the network graph of its state-space representations.

引用

下载

页码：912 / 927

页数：16

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