Some Applications of Watsonʹs Perturbation Approach to Random Matrices

In this note we draw attention to Watsonʹs (1983) perturbation approach to random matrices, by which the asymptotic distribution of eigenvalues and eigenvectors can be derived in a very elegant way. We extend his result to functions of matrices and give some applications in principal component analysis, multivariate analysis, and canonical correlations.