signEigenCSDA2006.pdf (146.85 kB)
Download fileSign eigenanalysis and its applications to optimization problems and robust statistics
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
© ElsevierVersion
- AM (Accepted Manuscript)
Publication date
2006Notes
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/S0167947304002324ISSN
0167-9473Publisher version
Language
- en