In this article, we propose a new multiple test procedure for assessing multivariate normality, which combines BHEP (Baringhaus–Henze–Epps–Pulley) tests by considering extreme and nonextreme choices of the tuning parameter in the definition of the BHEP test statistic. Monte Carlo power comparisons indicate that the new test presents a reasonable power against a wide range of alternative distributions, showing itself to be competitive against the most recommended procedures for testing a multivariate hypothesis of normality. We further illustrate the use of the new test for the Fisher Iris dataset
A Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. First, mu...
AbstractA Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. F...
Multivariate statistical methods often require the assumption of multivariate normality. The purpose...
AbstractLetX1, …, Xnbe i.i.d. randomd-vectors,d⩾1, with sample meanXand sample covariance matrixS. F...
The BHEP (Baringhaus--Henze--Epps--Pulley) test for assessing univariate and multivariate normality ...
We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based...
We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based...
A multiple test procedure for assessing multivariate normality (MVN) is proposed. The new test combi...
Testing multivariate normality is an ever-lasting interest in the goodness-of-fit area since the cla...
We study the empirical size and power of some recently proposed tests for multivariate normality (MV...
We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate d...
Methods of assessing the degree to which multivariate data deviate from multinormality are discussed...
In this paper, a goodness-of-fit test for normality based on the comparison of the theoretical and e...
Statistical analysis frequently relies on the assumption of normality. Though normality may often be...
In this thesis we introduce, implement and compare several multivariate goodness-of-fit tests. First...
A Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. First, mu...
AbstractA Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. F...
Multivariate statistical methods often require the assumption of multivariate normality. The purpose...
AbstractLetX1, …, Xnbe i.i.d. randomd-vectors,d⩾1, with sample meanXand sample covariance matrixS. F...
The BHEP (Baringhaus--Henze--Epps--Pulley) test for assessing univariate and multivariate normality ...
We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based...
We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based...
A multiple test procedure for assessing multivariate normality (MVN) is proposed. The new test combi...
Testing multivariate normality is an ever-lasting interest in the goodness-of-fit area since the cla...
We study the empirical size and power of some recently proposed tests for multivariate normality (MV...
We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate d...
Methods of assessing the degree to which multivariate data deviate from multinormality are discussed...
In this paper, a goodness-of-fit test for normality based on the comparison of the theoretical and e...
Statistical analysis frequently relies on the assumption of normality. Though normality may often be...
In this thesis we introduce, implement and compare several multivariate goodness-of-fit tests. First...
A Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. First, mu...
AbstractA Monte Carlo power study of 10 multivariate normality goodness-of-fit tests is presented. F...
Multivariate statistical methods often require the assumption of multivariate normality. The purpose...