Multivariate normality testing plays a critical role in modern statistical analysis by evaluating whether a multivariate dataset conforms to the assumptions of a normal distribution. Such assessments ...

This repository contains the code and analysis for a project focused on exploring multivariate normality within a dataset. The report's objective is to conduct a detailed analysis to determine whether ...

In many applications, the manifest variables are not even approximately multivariate normal. If this happens to be the case with your data set, the default generalized least-squares and maximum ...

ABSTRACT: The aim of this paper is to present a generalization of the Shapiro-Wilk W-test or Shapiro-Francia W'-test for application to two or more variables. It consists of calculating all the ...

ABSTRACT: In a previous article, an R script was developed and divided into three parts to implement the multivariate normality (MVN) Q-test based on both the chi-square approximation and the ...

Multivariate models more general than the standard multivariate linear model have received considerable attention in both the statistical and econometric literature; see Srivastava (1966, 1967, 1968) ...

For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the ...