Asymptotic distributions of functions of the eigenvalues of sample covariance matrix and canonical correlation matrix in multivariate time series

DOI10.1016/0047-259X(87)90083-2zbMath0618.62089MaRDI QIDQ1822435

Yanyan Li

Publication date: 1987

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

asymptotic expansioneigenvaluessignal processingjoint distributionGaussian stationary processmultivariate time seriesnon-Gaussiandiagonal matrixsample canonical correlationsasymptotic joint normality

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20)

Related Items (4)

Principal component analysis with autocorrelated data ⋮ Some recent trends in embeddings of time series and dynamic networks ⋮ Sparse principal component analysis for high‐dimensional stationary time series ⋮ Minimax estimation for time series models

Cites Work

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• Third order asymptotic properties of maximum likelihood estimators for Gaussian ARMA processes
• A central limit theorem for stationary processes and the parameter estimation of linear processes
• Asymptotic distributions of functions of the eigenvalues of some random matrices for nonnormal populations
• Asymptotic expansions of the distributions of the latent roots and the latent vector of the Wishart and multivariate F matrices
• Some recent developments on complex multivariate distributions
• On the asymptotic joint distributions of certain functions of the eigenvalues of four random matrices
• On detection of the number of signals in presence of white noise
• On the second order asymptotic efficiency of estimators of Gaussian ARMA processes
• Generalized canonical analysis for time series

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