Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

被引:60
|
作者
Arellano-Valle, Reinaldo B. [1 ]
Contreras-Reyes, Javier E. [2 ]
Genton, Marc G. [3 ]
机构
[1] Pontificia Univ Catolica Chile, Dept Estadist, Santiago, Chile
[2] Univ Chile, Dept Ingn Matemat, Santiago, Chile
[3] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
基金
美国国家科学基金会;
关键词
elliptical distribution; entropy; information theory; optimal network design; Shannon; skew-normal; skew-t; MONITORING NETWORK; OPTIMIZATION;
D O I
10.1111/j.1467-9469.2011.00774.x
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
. The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile.
引用
收藏
页码:42 / 62
页数:21
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