DocumentCode :
3123197
Title :
An innovative approach for analysing rank deficient covariance matrices
Author :
Tucci, Gabriel H. ; Wang, Ke
Author_Institution :
Bell Labs., Alcatel-Lucent, Murray, NJ, USA
fYear :
2012
fDate :
1-6 July 2012
Firstpage :
2596
Lastpage :
2600
Abstract :
The estimation of a covariance matrix from an insufficient amount of data is one of the most common problems in fields as diverse as multivariate statistics, wireless communications, signal processing, biology, learning theory and finance. In [13], a new approach to handle rank deficient covariance matrices was suggested. The main idea was to use dimensionality reduction in conjunction with an average over the Stiefel manifold. In this paper we further continue in this direction and consider a few innovative methods that show considerable improvements with respect to more traditional ones such as diagonal loading. One of the methods is called the Ewens estimator and uses a randomization of the sample covariance matrix over all the permutation matrices with respect to the Ewens measure. The techniques used to attack this problem are broad and run from random matrix theory to combinatorics.
Keywords :
covariance matrices; estimation theory; statistical analysis; Ewens estimator; Stiefel manifold; analysing rank deficient covariance matrices; covariance matrix; diagonal loading; innovative approach; multivariate statistics; randomization; signal processing; wireless communications; Covariance matrix; Educational institutions; Eigenvalues and eigenfunctions; Estimation; Loading; Mathematical model; Symmetric matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
Conference_Location :
Cambridge, MA
ISSN :
2157-8095
Print_ISBN :
978-1-4673-2580-6
Electronic_ISBN :
2157-8095
Type :
conf
DOI :
10.1109/ISIT.2012.6283987
Filename :
6283987
Link To Document :
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