Limiting behavior of eigenvalues in high-dimensional MANOVA via RMT (Q1991686)

The authors derive the asymptotic joint distribution of the eigenvalues under the null case and the local alternative cases in the MANOVA model and multiple discriminant analysis when both the dimension and the sample size are large. They apply methods from random matrix theory and do not assume normality of the population. It was shown that the null and nonnull distributions of the eigenvalues and invariant test statistics are asymptotically robust against departure from normality in high-dimensional situations.