Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

Reinaldo B. Arellano-Valle, Javier E. Contreras-Reyes, Marc G. Genton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)42-62
Number of pages21
JournalScandinavian Journal of Statistics
Issue number1
StatePublished - Mar 2013


  • Elliptical distribution
  • Entropy
  • Information theory
  • Optimal network design
  • Shannon
  • Skew-normal
  • Skew-t

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions'. Together they form a unique fingerprint.

Cite this