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Sign eigenanalysis and its applications to optimization problems and robust statistics

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journal contribution
posted on 15.12.2011, 14:11 by Baibing LiBaibing Li
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.

History

School

  • Business and Economics

Department

  • Business

Citation

LI, B., 2006. Sign eigenanalysis and its applications to optimization problems and robust statistics. Computational Statistics and Data Analysis, 50 (1), pp. 154 -162.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publication date

2006

Notes

This article was published in the journal, Computational Statistics and Data Analysis [© Elsevier]. The definitive version is available from: http://www.sciencedirect.com/science/article/pii/S0167947304002324

ISSN

0167-9473

Language

en