School of Mathematical Sciences, Zhejiang University, Hangzhou, China.
PLoS One. 2018 Mar 16;13(3):e0194382. doi: 10.1371/journal.pone.0194382. eCollection 2018.
Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows.
非均匀嵌入的转移熵是推断动力子系统之间因果关系的一种流行工具。在这项研究中，我们提出了一种方法，该方法利用低维条件互信息量在非均匀嵌入的搜索过程中对原始高维条件互信息进行分解，以找到不同时滞的显著变量。我们进行了一系列模拟实验，以评估我们提出的方法对不同变量的敏感性和特异性，以证明其与先前算法相比的优势。结果提供了具体的证据，表明低维近似可以帮助提高多元因果分析中转移熵的统计准确性，并在其他方法中表现出更好的性能。随着数据长度的增长，该方法尤其有效。
