Principal components selection given extensively many variables

Principal components analysis relates to the eigenvalue distribution of Wishart matrices. Given few observations and very many variables this distribution maps to eigenvalue statistics in the Gaussian orthogonal ensemble. Principal components selection can then be based on existing analytical results.