The mean square discrepancy of scrambled (T, S)-sequences

The discrepancy arises in the worst-case error analysis for quasi-Monte Car lo quadrature rules. Low discrepancy sets yield good quadrature rules. This article considers the mean square discrepancies for scrambled (lambda, t, m, s)-nets and (t, s)-sequences in base b. It is found that the mean square discrepancy for scrambled nets and sequences is never more than a constant multiple of that under simple Monte Carlo sampling. If the reproducing ker nel defining the discrepancy satis es a Lipschitz condition with respect to one of its variables separately, then the asymptotic order of the root mea n square discrepancy is O(n(-1)[logn]((s-1)/2)) for scrambled nets. If the reproducing kernel satis es a Lipschitz condition with respect to both of i ts variables, then the asymptotic order of the root mean square discrepancy is O(n(-3/2)[log n]((s-1)/2)) for scrambled nets. For an arbitrary number of points taken from a (t,s)-sequence, the root mean square discrepancy app ears to be no better than O(n(-1)[log n]((s-1)/2)), regardless of the smoot hness of the reproducing kernel.