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Graphical Comparison of Covariance Matrices
Australian Journal of Statistics, 1981SummaryProcedures for comparing within‐group covariance matrices are developed, based on separate analyses of the variances and of the correlations. The variances and the correlations are represented as two two‐way tables, with the columns representing groups. Graphical procedures based on comparisons of linear regressions are presented, by considering
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1995
It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
A standard problem in many classification tasks is how to model feature vectors whose elements are highly correlated. If multi-variate Gaussian distributions are used to model the data then they must have full covariance matrices to accurately do so. This requires a large number of parameters per distribution which restricts the number of distributions
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A standard problem in many classification tasks is how to model feature vectors whose elements are highly correlated. If multi-variate Gaussian distributions are used to model the data then they must have full covariance matrices to accurately do so. This requires a large number of parameters per distribution which restricts the number of distributions
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Three‐mode analysis of multimode covariance matrices
British Journal of Mathematical and Statistical Psychology, 2003Multimode covariance matrices, such as multitrait‐multimethod matrices, contain the covariances of subject scores on variables for different occasions or conditions. This paper presents a comparison of three‐mode component analysis and three‐mode factor analysis applied to such covariance matrices.
Kroonenberg, P.M., Oort, F.J.
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Testing Pattern Hypotheses for Covariance Matrices
Psychometrika, 1974Maximum likelihood estimates of the free parameters, and an asymptotic likelihood-ratio test, are given for the hypothesis that one or more elements of a covariance matrix are zero, and/or that two or more of its elements are equal. The theory applies immediately to a transformation of the covariance matrix by a known nonsingular matrix.
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Spectral Properties of Sample Covariance Matrices
Theory of Probability & Its Applications, 1996Summary: The expectation value of the resolvents of sample covariance matrices and the variance of their matrix elements are investigated. It is assumed only that variables have zero expectation values and the maximal fourth moment of variables exists. The principal spectral equations obtained earlier only in the form of limit formulas are derived with
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Ordering of Covariance Matrice
Econometric Theory, 1996CAPPUCCIO, NUNZIO, LUBIAN, DIEGO
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A well-conditioned estimator for large-dimensional covariance matrices
, 2004Olivier Ledoit, Michael Wolf
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Structured covariance matrices
SEG Technical Program Expanded Abstracts 1987, 1987John Parker Burg, Gary Mavko
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