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. If the function $h(\boldsymbol{x})$ and its mixed partial derivatives do not grow too fast as the Euclidean norm $|\boldsymbol{x}|$ goes to infinity, we obtain an error rate of $O(n^{-1+\epsilon})$ for QMC and randomized QMC (RQMC) with a sample size $n$ and an arbitrarily small $\epsilon>0$. However, the rate turns out to be $O(n^{-1+2M+\epsilon})$ if the functions grow exponentially with a rate of $O(\exp\{M|\boldsymbol{x}|^2\})$ for a constant $M\in(0,1/2)$. Superisingly, we find that using importance sampling with t distribution as the proposal can improve the root mean squared error of RQMC from $O(n^{-1+2M+\epsilon})$ to $O( n^{-3/2+\epsilon})$ for any $M\in(0,1/2)$.