A powerful test for multivariate normality

A new test for multivariate normality

We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate distributions based on Euclidean distance between sample elements. The proposed test applies to any multivariate distribution with finite second moments. In this article we apply the new method for testing multivariate normality when parameters are estimated. The resulting test is affine invariant...

An affine invariant multiple test procedure for assessing multivariate normality

Amultiple test procedure for assessing multivariate normality (MVN) that combines a finite set of affine invariant test statistics for MVN is proposed. This combination is based on a method introduced by Fromont and Laurent (Ann. Statist., 680–720, 34, 2006) that can be viewed as an improvement of the classical Bonferroni’s method. The usefulness of such approach is illustrated through a multip...

Outlier test for a group of multivariate observations

Assume that we have m independent random samples each of size n from Np(; ) and our goal is to test whether or not the ith sample is an outlier (i=1,2,…..m). To date it is well known that a test statistics exist whose null distribution is Betta and given the relationship between Betta and F distribution, an F test statistic can be used. In the statistical literature however a clear and preci...

ReductionCriterion and Multivariate Normality

A new test for multivariate normality by combining extreme and nonextreme BHEP tests

In this paper we propose a new multiple test procedure for assessing multivariate normality which combines BHEP (Baringhaus-Henze-Epps-Pulley) tests by considering extreme and non-extreme choices of the tuning parameter in the definition of the BHEP test statistic. Monte Carlo power comparisons indicate that the new test presents a reasonable power against a wide range of alternative distributi...