Non-parametric estimation of copula based mutual information

被引：2

作者：

Krishnankutty, Baby Alpettiyil ^{[1
]}

Ganapathy, Rajesh ^{[1
]}

Sankaran, Paduthol Godan ^{[1
]}

机构：

[1] Univ Sci & Technol, Dept Stat Cochin, Cochin 22, Kerala, India

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2020年 / 49卷 / 06期

关键词：

mutual information; copula entropy; non-parametric estimation; kernel; DENSITY; ASSOCIATION; ENTROPY;

D O I：

10.1080/03610926.2018.1563180

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Mutual information is a measure for investigating the dependence between two random variables. The copula based estimation of mutual information reduces the complexity because it is depend only on the copula density. We propose two estimators and discuss the asymptotic properties. To compare the performance of the estimators a simulation study is carried out. The methods are illustrated using real data sets.

引用

页码：1513 / 1527

页数：15

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