All courses for every first-year Science student will be delivered online this fall. A limited number of students in their second, third and fourth years will return to campus for part of the semester.

Statistics Seminar - Nikola Pocuca - Assessing and Visualizing Matrix Variate Normality


Location HH 305

Speaker: Nikola Pocuca (McMaster University)

Title: Assessing and Visualizing Matrix Variate Normality

Abstract:     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.
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McMaster University - Faculty of Science | Math & Stats