We prove the Law of Large Numbers and the Central Limit Theorem for analogs of U- and V-(von Mises) statistics of eigenvalues of random matrices as their size tends to in nity. We show rst that for a certain class of test functions (kernels), determining the statistics, the validity of these limiting laws reduces to the validity of analogous facts for certain linear eigenvalue statistics. We then check the conditions of the reduction statements for several most known ensembles of random matrices, The reduction phenomenon is well known in statistics, dealing with i.i.d. random variables. It is of interest that an analogous phenomenon is also the case for random matrices, whose eigenvalues are strongly dependent even if the entries of matrices are independent. 1

Central limit theorem for linear eigenvalue statistics of orthogonally invariant matrix models