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http://hdl.handle.net/1942/314
Title: | Transformation of Non Positive Semidefinite Correlation Matrices | Authors: | Rousseeuw, Peter J. MOLENBERGHS, Geert |
Issue Date: | 1993 | Source: | Communications in Statistics, Theory and Methods, 22(4), p. 965-984 | Abstract: | In multivariate statistics, estimation of the covariance or correlation matrix is of crucial importance. Computational and other arguments often lead to the use of coordinate-dependent estimators, yielding matrices that are symmetric but not positive semidefinite. We briefly discuss existing methods, based on shrinking, for transforming such matrices into positive semidefinite matrices. A simple method based on eigenvalues is also considered. Taking into account the geometric structure of correlation matrices, a new method is proposed which uses techniques similar to those of multidimensional scaling. | Keywords: | EIGENVALUE METHOD; MISSING DATA; MULTIDIMENSIONAL SCALING; MULTIVARIATE PROBIT MODEL; ROBUST CORRELATIONS; SHRINKING | Document URI: | http://hdl.handle.net/1942/314 | Link to publication/dataset: | https://www.researchgate.net/publication/232858104_Transformation_of_Non_Positive_Semidefinite_Correlation_Matrices https://www.academia.edu/8393364/Transformation_of_non_positive_semidefinite_correlation_matrices |
ISSN: | 0361-0926 | e-ISSN: | 1532-415X | DOI: | 10.1080/03610928308831068 | ISI #: | A1993KZ73700002 | Rights: | (C) 1993 by Marcel Dekker Inc. | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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