Abstract
Wishart matrix is one of typical random matrices and a constant times of a sample covariance matrix. The larger eigenvalues and corresponding eigenvectors of the sample covariance matrix are important to assess results from a sample in multivariate statistical analysis. On the other hand, the smaller ones are related to collinearity in a regression model. This paper discusses numerical computation for the istributions of the eigenvalues and the largest eigenvector for a Wishart matrix, and also show that approximations based on normal and chi-square distributions have high accuracy.
