随机正交表抽样设计的多元中心极限定理
A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments
摘要：
设$f:[0,1)^d \to {\mathbb R}$为可积函数。许多计算机实验的目的是通过在[0，1）^d中的有限数量的点处评估f来估计$\int_{[0,1)^d} f(x) dx$。在这些点的选择中存在设计问题，并且流行的选择是通过使用随机正交表。本文证明了一类随机正交表抽样设计[Owen（1992 a）]和一类基于OA的拉丁超立方体[Tang（1993）]的多元中心极限定理。
英文摘要：
Let $f:[0,1)^d \to {\mathbb R}$ be an integrable function. An objective of many computer experiments is to estimate $\int_{[0,1)^d} f(x) dx$ by evaluating f at a finite number of points in [0,1)^d. There is a design issue in the choice of these points and a popular choice is via the use of randomized orthogonal arrays. This article proves a multivariate central limit theorem for a class of randomized orthogonal array sampling designs [Owen (1992a)] as well as for a class of OA-based Latin hypercubes [Tang (1993)].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
