NORMUL: A FORTRAN program for testing multivariate normality

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...

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...

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...

Parametric Fortran - A Program Generator for Customized Generic Fortran Extensions

We describe the design and implementation of a program generator that can produce extensions of Fortran that are specialized to support the programming of particular applications. Extensions are specified through parameter structures that can be referred to in Fortran programs to specify the dependency of program parts on these parameters. By providing parameter values, a parameterized Fortran ...

ReductionCriterion and Multivariate Normality