On Latin hypercube sampling

• 成果类型: Article
• 署名作者: Loh, WL
• 署名单位: Purdue University System; Purdue University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1069362310
• 发表日期: 1996
• 页码: 2058-2080
• 关键词: CENTRAL-LIMIT-THEOREM; CONVERGENCE; remainder

摘要

This paper contains a collection of results on Latin hypercube sampling. The first result is a Berry-Esseen-type bound for the multivariate central limit theorem of the sample mean <(mu)over cap>(n) based on a Latin hypercube sample. The second establishes sufficient conditions on the convergence rate in the strong law for <(mu)over cap>(n). Finally motivated by the concept of empirical likelihood, a way of constructing nonparametric confidence regions based on Latin hypercube samples is proposed for vector means.