Non-parametric Wiener-Granger Causality in Partially Observed Systems

被引：0
|
作者：
Jafari-Mamaghani, Mehrdad [1 ]
机构：
[1] Stockholm Univ, Dept Math, Div Math Stat, S-10691 Stockholm, Sweden
来源：
2014 IEEE CONFERENCE ON NORBERT WIENER IN THE 21ST CENTURY (21CW) | 2014年
关键词：
Wiener-Granger causality; Conditional mutual information; Kernel canonical correlations;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Wiener's definition of causality, commonly known as Wiener-Granger causality, has become a frequently used quantification of temporally resolved causality in numerous fields of science. In many empirical studies, the system of interest cannot be observed in its entirety and relevant information may reside outside of the sampled observations. To this end, partial Wiener-Granger causality has been developed to circumvent this issue. In this paper, we extend partial Wiener-Granger causality to the non-parametric case and discuss different approaches to estimate it.
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页数：5
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