Testing Multivariate Normality Based on t-Representative Points

On Using Asymptotic Critical Values in Testing for Multivariate Normality

Many multivariate statistical methods call upon the assumption of multivariate normality. However, many researchers fail to test this assumption. This omission could be due either to ignorance of the existence of tests of multivariate normality or confusion about which test to use. Although at least 50 tests of multivariate normality exist, relatively little is known about the power of these pr...

On the Canonical-Based Goodness-of-fit Tests for Multivariate Skew-Normality

It is well-known that the skew-normal distribution can provide an alternative model to the normal distribution for analyzing asymmetric data. The aim of this paper is to propose two goodness-of-fit tests for assessing whether a sample comes from a multivariate skew-normal (MSN) distribution. We address the problem of multivariate skew-normality goodness-of-fit based on the empirical Laplace tra...

Finding Representative Points in Multivariate Data Using PCA

The idea of representation has been used in various fields of study from data analysis to political science. In this paper, we define representativeness and describe a method to isolate data points that can represent the entire data set. Also, we show how the minimum set of representative data points can be generated. We use data from GLOBE (a project to study the effects on Land Change based o...

Testing the Multivariate Normality of Australian Stock Returns

The multivariate normality of stock returns is a crucial assumption in many tests of assets pricing models. While past Australian research has examined the univariate normality of returns, univariate test statistics are unreliable for testing multivariate normality since they ignore the contemporaneous correlation between asset returns. This paper utilises a multivariate test procedure, based o...

ReductionCriterion and Multivariate Normality