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- W578738084 abstract "ABSTRACTMany multivariate statistical procedures are based on the assumption of normality and different approaches have been proposed for testing this assumption. The vast majority of these tests, however, are exclusively designed for cases when the sample size n is larger than the dimension of the variable p, and the null distributions of their test statistics are usually derived under the asymptotic case when p is fixed and n increases. In this article, a test that utilizes principal components to test for nonnormality is proposed for cases when p/n → c. The power and size of the test are examined through Monte Carlo simulations, and it is argued that the test remains well behaved and consistent against most nonnormal distributions under this type of asymptotics." @default.
- W578738084 created "2016-06-24" @default.
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- W578738084 date "2016-06-13" @default.
- W578738084 modified "2023-09-24" @default.
- W578738084 title "Using principal components to test normality of high-dimensional data" @default.
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- W578738084 doi "https://doi.org/10.1080/03610918.2015.1089286" @default.
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