Orthogonal Array-Based Latin Hypercubes
本文利用正交阵列构造拉丁超立方体，不仅保留一维分层特性，还能对每个r维边缘进行分层，为计算机实验和数值积分提供更优的设计，并证明其在积分中显著优于普通拉丁超立方体抽样。
Abstract In this article, we use orthogonal arrays (OA's) to construct Latin hypercubes. Besides preserving the univariate stratification properties of Latin hypercubes, these strength r OA-based Latin hypercubes also stratify each r-dimensional margin. Therefore, such OA-based Latin hypercubes provide more suitable designs for computer experiments and numerical integration than do general Latin hypercubes. We prove that when used for integration, the sampling scheme with OA-based Latin hypercubes offers a substantial improvement over Latin hypercube sampling.