| Abstract: | Royston proposed a normal probability plot to detect nonnormality of univariate data. The normal probability plot was provided with normalized acceptance regions to enhance its interpretability. By using the theory of spherical distributions and the idea of principal component analysis, we propose an approach to extending Royston’s normal plot to detecting nonmultivariate normality in analyzing high-dimensional data. The performance of the proposed multivariate normal plot is demonstrated by Monte Carlo studies and illustrated by two real datasets. Datasets, computer code and documentation of the code are available in the online supplements. |
