
Accession Number : ADA332223
Title : An Overall Test for Multivariate Normality.
Descriptive Note : Technical rept.,
Corporate Author : PENNSYLVANIA STATE UNIV UNIVERSITY PARK CENTER FOR MULTIVARIATE ANALYSIS
Personal Author(s) : Rao, C. R. ; Ali, Hydar
PDF Url : ADA332223
Report Date : SEP 1997
Pagination or Media Count : 9
Abstract : There are a number of methods in the statistical literature for testing whether observed data came from a multivariate normal(MVN) distribution with an unknown mean vector and covariance matrix. Let X1, ... be an iid sample of size n from a pvariate normal distribution. Denote the sample mean and sample variancecovariance matrix by X and S respectively. Most of the tests of multivariate normality are based on the results that YiS1/2(Xi  X), i=1,.., n, are asymptotically iid as pvariate normal than zero mean vector and identity covariance matrix. Tests developed by Andrews et al., Mardina and others are direct functions of Yi. We note that the N=np components of the Yi's put together can be considered as an asymptotically iid sample of size N from a univariate normal any well known test based on N independent observations for univariate normality. In Particular we can use univariate skewness and kurtosis tests, which are sensitive to deviations from normality.
Descriptors : *MULTIVARIATE ANALYSIS, MATRICES(MATHEMATICS), STATISTICS, SENSITIVITY, VARIATIONS, COVARIANCE, STATISTICAL DISTRIBUTIONS, SKEWNESS, FUNCTIONS(MATHEMATICS), NORMALITY.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE