The filtered nearest neighbor method for generating low-discrepancy sequences

We introduce the filtered nearest neighbor method for generating low-discrepancy random-number sequences. Simulations show that these sequences have lower discrepancy than either pseudo random or quasi random numbers when used to generate small samples (N≤300) in high dimensions (p≥24). They are therefore useful for approximating high-dimensional expectation integrals when function evaluation is expensive.