基于copula熵的互信息估计方法-控制理论与应用.pdfVIP

30 7 Vol. 30 No. 7 2013 7 Control Theory Applications Jul. 2013 DOI: 10.7641/CTA.2013.21262 Copula , ( , 116023 ) : , , . , , Copula . Copula, Copula. Kendall Copula. . , . : ; Copula; ; ; : TP181 : A Mutual information estimation based on Copula entropy HAN Min , LIU Xiao-xin (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian Liaoning 116023, China) Abstract: Mutual information is commonly used in the measure of dependency between variables. However, in the estimation of mutual information, the estimation of joint probability density function is always a hard problem. To avoid the estimation of joint probability density function and to improve both the effectiveness and efficiency of the estimation of mutual information, we propose a novel mutual information estimation method based on the entropy of the Copula density function. The estimation of mutual information is transformed into the estimation of Copula entropy by taking advantages of their relationships. Parameter estimation method based on Kendall’s rank correlation coefficient is used for the estimation of the parameters in Copula function. The proposed method is compared with the following four methods: histogram method, kernel method, nearest neighbor method and maximum likelihood method. Simulation results on the two dimensional Gaussian distribution data substantiate the effectiveness and efficiency of the proposed method. Key words: mutual information; Copula; entropy; probability density function; parameter estimation 1 (Introduction)