期刊
ENTROPY
卷 17, 期 6, 页码 4173-4201出版社
MDPI AG
DOI: 10.3390/e17064173
关键词
entropy estimation; k nearest neighbors; transfer entropy; bias reduction
This paper deals with the estimation of transfer entropy based on the k-nearest neighbors (k-NN) method. To this end, we first investigate the estimation of Shannon entropy involving a rectangular neighboring region, as suggested in already existing literature, and develop two kinds of entropy estimators. Then, applying the widely-used error cancellation approach to these entropy estimators, we propose two novel transfer entropy estimators, implying no extra computational cost compared to existing similar k-NN algorithms. Experimental simulations allow the comparison of the new estimators with the transfer entropy estimator available in free toolboxes, corresponding to two different extensions to the transfer entropy estimation of the Kraskov-Stogbauer-Grassberger (KSG) mutual information estimator and prove the effectiveness of these new estimators.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据