This article gives a synopsis on new developments in affine invariant tests for multivariate
normality in an iid-setting, with special emphasis on asymptotic properties of several classes …

New invariant and consistent goodness-of-fit tests for multivariate normality are introduced.
Tests are based on the Karhunen–Loève transformation of a multidimensional sample from …

Testing multivariate normality is an ever-lasting interest in the goodness-of-fit area since the
classical Pearson's chi-squared test. Among the numerous approaches in the construction of …

The multivariate normal is a common assumption in many statistical models and
methodologies for high-dimensional data analysis. The exploration of approaches to testing …

We generalize a recent class of tests for univariate normality that are based on the empirical
moment generating function to the multivariate setting, thus obtaining a class of affine …

The assumption of normality has underlain much of the development of statistics, including
spatial statistics, and many tests have been proposed. In this work, we focus on the …

In this paper, an alternative discrete skew Laplace distribution is proposed, which is derived
by using the general approach of discretizing a continuous distribution while retaining its …

Based on a chi square transform of the multivariate normal data set, we proposed a
technique for testing multinormality which is the sum of interpoint squared distances …

A novel Bayesian nonparametric test for assessing multivariate normal models is presented.
Although there are extensive frequentist and graphical methods for testing multivariate …

In this paper, we propose an alternative generalization of a recent test for univariate
normality which is based on the empirical moment generating function to the multivariate …