Using principal components to test normality of high-dimensional data

DOI10.1080/03610918.2015.1089286zbMath1422.62196OpenAlexW578738084MaRDI QIDQ4976532

Rashid Mansoor

Publication date: 31 July 2017

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2015.1089286

zbMATH Keywords

principal componentsnonnormalityincreasing dimension

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)

Related Items (1)

Estimation of multivariate 3rd moment for high-dimensional data and its application for testing multivariate normality

Cites Work

• A comparative study of goodness-of-fit tests for multivariate normality
• A new approach to the BHEP tests for multivariate normality
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• Biometrika: The first 100 years
• Some Techniques for Assessing Multivarate Normality Based on the Shapiro- Wilk W
• Measures of multivariate skewness and kurtosis for tests of nonnormality
• Tests of Linearity, Multivariate Normality and the Adequacy of Linear Scores
• Assessing Normality of High-Dimensional Data
• An analysis of variance test for normality (complete samples)
• Measures of multivariate skewness and kurtosis with applications

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