Limiting spectral distribution of large sample covariance matrices associated with a class of stationary processes

DOI10.1007/s10959-013-0508-xzbMath1325.60043OpenAlexW2052613069MaRDI QIDQ495709

Marwa Banna, Florence Merlevède

Publication date: 15 September 2015

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

Full work available at URL: https://doi.org/10.1007/s10959-013-0508-x

zbMATH Keywords

stationary processesweak dependencelimiting spectral distributionsample covariance matricesLindeberg methodMarchenko-Pastur distributions

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Stationary stochastic processes (60G10) Limit theorems in probability theory (60F99)

Related Items

CLT for linear spectral statistics of large dimensional sample covariance matrices with dependent data ⋮ Almost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices ⋮ Central limit theorem for linear eigenvalue statistics for a tensor product version of sample covariance matrices ⋮ Nonparametric estimate of spectral density functions of sample covariance matrices generated by VARMA models ⋮ Asymptotic normality in Banach spaces via Lindeberg method ⋮ Operator-valued matrices with free or exchangeable entries ⋮ Singular value distribution of dense random matrices with block Markovian dependence ⋮ On the empirical spectral distribution for matrices with long memory and independent rows ⋮ High-dimensional linear models: a random matrix perspective ⋮ Large sample correlation matrices: a comparison theorem and its applications ⋮ On the limiting spectral distribution for a large class of symmetric random matrices with correlated entries

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