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On Integration Methods Based on Scrambled Nets of Arbitrary Size

We consider the problem of evaluating $I(φ):=\int_{[0,1)^s}φ(x) dx$ for a function $φ\in L^2[0,1)^{s}$. In situations where $I(φ)$ can be approximated by an estimate of the form $N^{-1}\sum_{n=0}^{N-1}φ(x^n)$, with $\{x^n\}_{n=0}^{N-1}$ a point set in $[0,1)^s$, it is now well known that the $O_P(N^{-1/2})$ Monte Carlo convergence rate can be improved by taking for $\{x^n\}_{n=0}^{N-1}$ the first $N=λb^m$ points, $λ\in\{1,\dots,b-1\}$, of a scrambled $(t,s)$-sequence in base $b\geq 2$. In this paper we derive a bound for the variance of scrambled net quadrature rules which is of order $o(N^{-1})$ without any restriction on $N$. As a corollary, this bound allows us to provide simple conditions to get, for any pattern of $N$, an integration error of size $o_P(N^{-1/2})$ for functions that depend on the quadrature size $N$. Notably, we establish that sequential quasi-Monte Carlo (M. Gerber and N. Chopin, 2015, \emph{J. R. Statist. Soc. B, to appear.}) reaches the $o_P(N^{-1/2})$ convergence rate for any values of $N$. In a numerical study, we show that for scrambled net quadrature rules we can relax the constraint on $N$ without any loss of efficiency when the integrand $φ$ is a discontinuous function while, for sequential quasi-Monte Carlo, taking $N=λb^m$ may only provide moderate gains.