  Let X_n=(x_{ij}) be an n by p data matrix, where the n rows form a random
sample of size n from a certain p-dimensional population distribution.
  Let R_n=(\rho_{ij}) be the p\times p sample correlation matrix of X_n; that
is, the entry \rho_{ij} is the usual Pearson's correlation coefficient between
the ith column of X_n and jth column of X_n. For contemporary data both n and p
are large. When the population is a multivariate normal we study the test that
H_0: the p variates of the population are uncorrelated.
  A test statistic is chosen as L_n=max_{i\ne j}|\rho_{ij}|. The asymptotic
distribution of L_n is derived by using the Chen-Stein Poisson approximation
method. Similar results for the non-Gaussian case are also derived.
