Analysis of the limiting spectral measure of large random matrices of the separable covariance type
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Publication:2935253
DOI10.1142/S2010326314500166zbMath1305.15078arXiv1310.8094OpenAlexW2138136390MaRDI QIDQ2935253
Publication date: 22 December 2014
Published in: Random Matrices: Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1310.8094
Eigenvalues, singular values, and eigenvectors (15A18) Random matrices (algebraic aspects) (15B52) Limit theorems in probability theory (60F99)
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Cites Work
- Unnamed Item
- Almost sure localization of the eigenvalues in a Gaussian information plus noise model. Application to the spiked models.
- The empirical distribution of the eigenvalues of a Gram matrix with a given variance profile
- No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix
- Analysis of the limiting spectral distribution of large dimensional information-plus-noise type matrices
- No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
- Exact separation of eigenvalues of large dimensional sample covariance matrices
- Analysis of the limiting spectral distribution of large dimensional random matrices
- Tracy-Widom limit for the largest eigenvalue of a large class of complex sample covariance matrices
- Deterministic equivalents for certain functionals of large random matrices
- LARGE INFORMATION PLUS NOISE RANDOM MATRIX MODELS AND CONSISTENT SUBSPACE ESTIMATION IN LARGE SENSOR NETWORKS
- NO EIGENVALUES OUTSIDE THE SUPPORT OF THE LIMITING SPECTRAL DISTRIBUTION OF INFORMATION-PLUS-NOISE TYPE MATRICES
- Improved Estimation of Eigenvalues and Eigenvectors of Covariance Matrices Using Their Sample Estimates
- On the Asymptotic Behavior of the Sample Estimates of Eigenvalues and Eigenvectors of Covariance Matrices
- Improved Subspace Estimation for Multivariate Observations of High Dimension: The Deterministic Signals Case