Control Functionals for Quasi-Monte Carlo Integration

Abstract

Quasi-Monte Carlo (QMC) methods are being adopted in statistical applications due to the increasingly challenging nature of numerical integrals that are now routinely encountered. For integrands with $d$-dimensions and derivatives of order $\alpha$, an optimal QMC rule converges at a best-possible rate $O(N^{-\alpha/d})$. However, in applications the value of $\alpha$ can be unknown and/or a rate-optimal QMC rule can be unavailable. Standard practice is to employ $\alpha_L$-optimal QMC where the lower bound $\alpha_L \leq \alpha$ is known, but in general this does not exploit the full power of QMC. One solution is to trade-off numerical integration with functional approximation. This strategy is explored herein and shown to be well-suited to modern statistical computation. A challenging application to robotic arm data demonstrates a substantial variance reduction in predictions for mechanical torques.