2019/10/07 by Nikola Počuča, Nikola Pocuca, Michael P. B. Gallaugher +6
Computer Science · Engineering · Mathematics · #Diverse Scientific and Engineering Research #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Methodology (stat.ME) #Statistical and numerical algorithms #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.1910.02859
arxiv created 2019/10/07 · openalex publication_date 2019/10/07 · arxiv updated 2019/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A framework for assessing the matrix variate normality of three-way data is developed. The framework comprises a visual method and a goodness of fit test based on the Mahalanobis squared distance (MSD). The MSD of multivariate and matrix variate normal estimators, respectively, are used as an assessment tool for matrix variate normality. Specifically, these are used in the form of a distance-distance (DD) plot as a graphical method for visualizing matrix variate normality. In addition, we employ the popular Kolmogorov-Smirnov goodness of fit test in the context of assessing matrix variate normality for three-way data. Finally, an appropriate simulation study spanning a large range of dimensions and data sizes shows that for various settings, the test proves itself highly robust.