Normality Testing of High-Dimensional Data Based on Principle Component and Jarque–Bera Statistics

The testing of high-dimensional normality is an important issue and has been intensively studied in the literature, it depends on variance–covariance matrix sample numerous methods have proposed to reduce its complexity. Principle component analysis (PCA) widely used high dimensions, since can project data into a lower-dimensional orthogonal space. reduced then be evaluated by Jarque–Bera (JB) statistics each principle direction. We propose combined test statistic—the summation one-way JB upon independence directions—to multivariate dimensions. performance method illustrated empirical power simulated normal non-normal data. Two real examples show validity our method.