Testing for normality.

Performance analysis of powers of skewness and kurtosis based multivariate normality tests and use of extended Monte Carlo simulation for proposed novelty algorithm

An ample study of the comparative powers of a number of omnibus multivariate normality tests is main object in this paper. Since testing for multivariate normality tests is considerably more challenging process than for testing of univariate one and therefore, study of testing for multivariate normality tests has its increasing demand. Through this paper, we have explored several techniques for...

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...

Inferences on the Generalized Variance under Normality

Generalized variance is applied for determination of dispersion in a multivariate population and is a successful measure for concentration of multivariate data. In this article, we consider constructing confidence interval and testing the hypotheses about generalized variance in a multivariate normal distribution and give a computational approach. Simulation studies are performed to compare thi...

Significant interactions in two-way ANOVA: example on impact on testing for normality

A key issue in various applications of analysis of variance is testing for interaction and the interpretation of resulting analysis of variance tables. In this note is demonstrated that for a two-way ANOVA, incorporating interactions or not may have a dramatic influence when considering the usual statistical tests for normality of residuals. The effect of numerical rounding is also discussed.

Testing Normality Against The Laplace Distribution

MATHEMATICAL ENGINEERING TECHNICAL REPORTS Skewness and kurtosis as locally best invariant tests of normality

Consider testing normality against a one-parameter family of univariate distributions containing the normal distribution as the boundary, e.g., the family of t-distributions or an infinitely divisible family with finite variance. We prove that under mild regularity conditions, the sample skewness is the locally best invariant (LBI) test of normality against a wide class of asymmetric families a...