A distance between multivariate normal distributions based in an embedding into the siegel group
Miquel Calvo and
Josep M. Oller
Journal of Multivariate Analysis, 1990, vol. 35, issue 2, 223-242
Abstract:
This paper shows an embedding of the manifold of multivariate normal densities with informative geometry into the manifold of definite positive matrices with the Siegel metric. This embedding allows us to obtain a general lower bound for the Rao distance, which is itself a distance, and we suggest employing it for statistical purposes, taking into account the similitude of the above related metrics. Further-more, through this embedding, general statistical tests of hypothesis are derived, and some geometrical properties are studied too.
Keywords: information; metric; Siegel; geometry; geodesic; distance; for; probabilistic; models; multivariate; normal; distribution (search for similar items in EconPapers)
Date: 1990
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0047-259X(90)90026-E
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:35:y:1990:i:2:p:223-242
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Journal of Multivariate Analysis is currently edited by de Leeuw, J.
More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().