Asymptotic expansions for the distributions of statistics based on a correlation matrix

DOI10.2307/3314825zbMath0396.62035OpenAlexW2056776140MaRDI QIDQ4179686

Sadanori Konishi

Publication date: 1978

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3314825

zbMATH Keywords

Principal Component AnalysisAsymptotic ExpansionMultivariate Normal DistributionWishart DistributionCorrelation MatrixDimensionality ReductionLatent Root

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Asymptotic distribution theory in statistics (62E20)

Related Items (5)

Principal Eigenportfolios for U.S. Equities ⋮ Model-based principal components of correlation matrices ⋮ Effect of a Shrinkage Estimator on the Linear Discriminant Function ⋮ Asymptotic expansions for the distributions of functions of a correlation matrix ⋮ Second-order accurate inference on eigenvalues of covariance and correlation matrices

Cites Work

• Unnamed Item
• Unnamed Item
• On the evaluation of some distributions that arise in simultaneous tests for the equality of the latent roots of the covariance matrix
• On the ratios of the individual latent roots to the trace of a Wishart matrix
• On a statistic useful in dimensionality reduction in multivariable linear stochastic system
• Asymptotic Theory for Principal Component Analysis
• Derivatives of the characteristic root of a synmetric or a hermitian matrix with two applications in multivariate analysis

This page was built for publication: Asymptotic expansions for the distributions of statistics based on a correlation matrix