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Sign eigenanalysis and its applications to optimization problems and robust statistics

Lookup NU author(s): Dr Baibing Li


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Sign eigenvectors for a real square matrix, A, are defined to be sign vectors for which all of its elements either retain the same signs or become to their opposite signs after the linear transformation A, where a sign vector is a vector with the elements equal to either 1 or -1. Existence of sign eigenvectors for symmetric positive semi-definite matrices is investigated. It is shown that the sign eigenanalysis is closely related to some certain optimization problems and can be applied to develop robust statistical inference procedures in the L1 norm. A numerical example is given to illustrate the applications to robust multivariate statistical analysis. © 2004 Published by Elsevier B.V.

Publication metadata

Author(s): Li B

Publication type: Article

Publication status: Published

Journal: Computational Statistics and Data Analysis

Year: 2006

Volume: 50

Issue: 1

Pages: 154-162

ISSN (print): 0167-9473

ISSN (electronic): 1872-7352

Publisher: Elsevier BV


DOI: 10.1016/j.csda.2004.07.012


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