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Iterated Toeplitz approximation of covariance matrices
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003For a signal consisting of p complex exponentials in white noise, it is well known that the true covariance matrix will have the Hermitian Toeplitz structure and that its minimum eigenvalue will have a dimension of M-p (where M is the dimension of the matrix).
D. M. Wikes, Monson H. Hayes
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A Metric for Covariance Matrices
2003The paper presents a metric for positive definite covariance matrices. It is a natural expression involving traces and joint eigenvalues of the matrices. It is shown to be the distance coming from a canonical invariant Riemannian metric on the space Sym + (n, ℝ) of real symmetric positive definite ...
Wolfgang Förstner, Boudewijn Moonen
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Structural Analysis of Covariance and Correlation Matrices
Psychometrika, 1978A general approach to the analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest. The statistical problems of identification, estimation and testing of such covariance or correlation structures are discussed.Several ...
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2018
The tail dependence coefficient (TDC) is a natural tool to describe extremal dependence. Estimation of the tail dependence coefficient can be performed via empirical process theory. In case of extremal independence, the limit degenerates and hence one cannot construct a test for extremal independence.
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The tail dependence coefficient (TDC) is a natural tool to describe extremal dependence. Estimation of the tail dependence coefficient can be performed via empirical process theory. In case of extremal independence, the limit degenerates and hence one cannot construct a test for extremal independence.
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Ordering of Covariance Matrice
Econometric Theory, 1996CAPPUCCIO, NUNZIO, LUBIAN, DIEGO
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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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Analytic inversion of a class of covariance matrices
IEEE Transactions on Information Theory, 1960The sample covariance matrix arising out of finite memory linear least squares estimation over a set of equally spaced time points, is inverted by spectral methods (operationally referred to as the z transform). It is shown that the complexity of the problem depends only upon the complexity of the input correlation function. The final solution is shown
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Analog Computation of Covariance Matrices
IEEE Transactions on Electronic Computers, 1961T. W. Connolly, Kenneth S. Miller
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High-Dimensional Dynamic Covariance Matrices With Homogeneous Structure
Journal of Business and Economic Statistics, 2022Yuan Ke, Heng Lian
exaly
Structured covariance matrices
SEG Technical Program Expanded Abstracts 1987, 1987John Parker Burg, Gary Mavko
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