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Title: Sign eigenanalysis and its applications to optimization problems and robust statistics
Authors: Li, Baibing
Keywords: Eigenproblem
L1 norm
Optimization
Robust statistical inference
Issue Date: 2006
Publisher: © Elsevier
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.
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1016/j.csda.2004.07.012
URI: https://dspace.lboro.ac.uk/2134/9223
Publisher Link: http://www.sciencedirect.com/science/article/pii/S0167947304002324
ISSN: 0167-9473
Appears in Collections:Published Articles (Business School)

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