Quasi-Monte Carlo for unbounded integrands with importance sampling

We consider the problem of estimating an expectation $ \mathbb{E}\left[ h(W)\right]$ by quasi-Monte Carlo (QMC) methods, where $ h $ is an unbounded smooth function on $ \mathbb{R}^d $ and $ W$ is a standard normal distributed random variable. To study rates of convergence for QMC on unbounded integrands, we use a smoothed projection operator to project the output of $W$ to a bounded region, which differs from the strategy of avoiding the singularities along the boundary of the unit cube $ [0,1]^d $ in 10.1137/S0036144504441573. The error is then bounded by the quadrature error of the transformed integrand and the projection error. Under certain growth conditions on the function $h$, we obtain an error rate of $ O(n^{-1+\epsilon}) $ for QMC and randomized QMC with a sample size $ n $ and an arbitrarily small $ \epsilon>0 $. Furthermore, we find that importance sampling can improve the root mean squared error of randomized QMC from $ O(n^{-1+\epsilon}) $ to $ O( n^{-3/2+\epsilon}) $.