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We study approximate integration of a function f over [0,1]^s based on taking the median of 2r-1 integral estimates derived from independently randomized (t,m,s)-nets in base 2. The nets are randomized by Matousek's random linear scramble with a random digital shift. If f is analytic over [0,1]^s, then the probability that any one randomized net's estimate has an error larger than 2^{-cm^2/s} times a quantity depending on f is O(1/\sqrt {m}) for any c<3\log (2)/\pi ^2\approx 0.21. As a result, the median of the distribution of these scrambled nets has an error that is O(n^{-c\log (n)/s}) for n=2^m function evaluations. The sample median of 2r-1 independent draws attains this rate too, so long as r/m^2 is bounded away from zero as m\to \infty. We include results for finite precision estimates and some nonasymptotic comparisons to taking the mean of 2r-1 independent draws. [ABSTRACT FROM AUTHOR]

Let f be analytic on [0,1] with |f^{(k)}(1/2)|\leqslant A\alpha ^kk! for some constants A and \alpha <2 and all k\geqslant 1. We show that the median estimate of \mu =\int _0^1f(x)\,\mathrm {d} x under random linear scrambling with n=2^m points converges at the rate O(n^{-c\log (n)}) for any c< 3\log (2)/\pi ^2\approx 0.21. We also get a super-polynomial convergence rate for the sample median of 2k-1 random linearly scrambled estimates, when k/m is bounded away from zero. When f has a p'th derivative that satisfies a \lambda-Hölder condition then the median of means has error O(n^{-(p+\lambda)+\epsilon }) for any \epsilon >0, if k\to \infty as m\to \infty. The proof techniques use methods from analytic combinatorics that have not previously been applied to quasi-Monte Carlo methods, most notably an asymptotic expression from Hardy and Ramanujan on the number of partitions of a natural number. [ABSTRACT FROM AUTHOR]