A characterization of multivariate normality through univariate projections

DOI10.1016/j.jmva.2010.04.015zbMath1198.62045OpenAlexW2020763192WikidataQ42872587 ScholiaQ42872587MaRDI QIDQ604376

Yongzhao Shao, Ming Zhou

Publication date: 10 November 2010

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

Full work available at URL: http://europepmc.org/articles/pmc3837532

zbMATH Keywords

non-normalitymultivariate normal distributiongoodness of fitmarginal distributionlinear combination of components

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Exact distribution theory in statistics (62E15) Characterization and structure theory of statistical distributions (62E10)

Related Items (6)

A note on Gaussian distributions in \(\mathbb R^n\) ⋮ On some counter examples of the bivariate and multivariate normal distributions: A brief survey ⋮ Univariate likelihood projections and characterizations of the multivariate normal distribution ⋮ Letter to the editor ⋮ Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data ⋮ A powerful test for multivariate normality

Cites Work

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• Characterization of the \(p\)-generalized normal distribution
• The multivariate normal distribution
• The non-singularity of generalized sample covariance matrices
• On the Determination of the Bivariate Normal Distribution from Distributions of Linear Combinations of the Variables
• Measure Theory and Probability Theory
• Establishing the Positive Definiteness of the Sample Covariance Matrix

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