Normalizing and variance stabilizing transformations of multivariate statistics under an elliptical population

DOI10.1007/BF02491469zbMath0624.62049OpenAlexW2024659012MaRDI QIDQ578803

Takesi Hayakawa

Publication date: 1987

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02491469

zbMATH Keywords

canonical correlationsample covariance matrixsample correlationelliptical populationFisher's z- transformationlatent rootmultiple correlationnormalizing transformationvariance stabilizing transformations

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Asymptotic distribution theory in statistics (62E20) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (9)

On the distribution of the function of the F-matrix under an elliptical population ⋮ Asymptotic expansions for the distribution of quadratic forms in normal variables ⋮ The effects on the distributions of sample canonical correlations under nonnormality ⋮ Computer algebra application to the distribution of sample correlation coefficient ⋮ Asymptotic expansions in the singular value decomposition for cross covariance and correlation under nonnormality ⋮ Inferences on correlation coefficients in some classes of nonnormal distributions ⋮ On the asymptotic distributions of two statistics for two-level covariance structure models within the class of elliptical distributions ⋮ Perturbation inequalities and confidence sets for functions of a scatter matrix. ⋮ General saddlepoint approximations to distributions under an elliptical population

Cites Work

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• Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
• Normalizing transformations of some statistics in multivariate analysis
• Asymptotic distribution of the sample roots for a nonnormal population
• An approximation to the distribution of the sample correlation coefficient

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