Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions
Author
dc.contributor.author
Arellano Valle, Reinaldo B.
Author
dc.contributor.author
Contreras Reyes, Javier E.
es_CL
Author
dc.contributor.author
Genton, Marc G.
es_CL
Admission date
dc.date.accessioned
2014-03-13T19:37:05Z
Available date
dc.date.available
2014-03-13T19:37:05Z
Publication date
dc.date.issued
2013
Cita de ítem
dc.identifier.citation
Scand J Statist 40
en_US
Identifier
dc.identifier.other
doi: 10.1111/j.1467-9469.2011.00774.x
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/126454
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
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.