Almost sure localization of the eigenvalues in a Gaussian information plus noise model. Application to the spiked models.

DOI10.1214/EJP.v16-943zbMath1245.15039arXiv1009.5807OpenAlexW2165903157MaRDI QIDQ428560

Publication date: 22 June 2012

Published in: Electronic Journal of Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1009.5807

zbMATH Keywords

random matrix theory Gram matrix spiked models Gaussian information plus noise model localization of the eigenvalues

Mathematics Subject Classification ID

Random matrices (probabilistic aspects) (60B20) Strong limit theorems (60F15) Eigenvalues, singular values, and eigenvectors (15A18) Random matrices (algebraic aspects) (15B52)

Related Items

Statistical inference for principal components of spiked covariance matrices ⋮ A random matrix approach to neural networks ⋮ Analysis of the limiting spectral measure of large random matrices of the separable covariance type ⋮ Spectral analysis of the Gram matrix of mixture models ⋮ Tracy-Widom law for the extreme eigenvalues of large signal-plus-noise matrices ⋮ The limiting spectral distribution of large-dimensional general information-plus-noise-type matrices ⋮ Limiting Eigenvectors of Outliers for Spiked Information-Plus-Noise Type Matrices ⋮ On the almost sure location of the singular values of certain Gaussian block-Hankel large random matrices ⋮ A CLT for linear spectral statistics of large random information-plus-noise matrices ⋮ Large complex correlated Wishart matrices: fluctuations and asymptotic independence at the edges ⋮ Free probability of type B and asymptotics of finite-rank perturbations of random matrices ⋮ Outlier Eigenvalues for Deformed I.I.D. Random Matrices ⋮ Edge statistics of large dimensional deformed rectangular matrices ⋮ The random matrix regime of Maronna's M-estimator with elliptically distributed samples

This page was built for publication: Almost sure localization of the eigenvalues in a Gaussian information plus noise model. Application to the spiked models.