Enumeration and Evaluation of Small Orthogonal Latin Hypercube Designs for Polynomial Regression Models

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• 作者：Haralambos Evangelaras
• 刊名：Quality and Reliability Engineering International
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：32
• 期：7
• 页码：2381-2389
• 全文大小：165K
• ISSN：1099-1638

文摘

In this paper, we construct and evaluate all nonisomorphic Latin hypercube designs with n≤16 runs, the use of which guarantee that the estimates of the first-order effects are uncorrelated with each other and also uncorrelated with the estimates of the second-order effects, in polynomial regression models. The produced designs are evaluated using well-known and popular criteria, and optimal designs are presented in every case studied. An effort to construct nonisomorphic small Latin hypercubes in which only the estimates of the first-order effects are required to be uncorrelated with each other has also been made, and new designs are presented. All the constructed designs, besides their stand-alone properties, are useful for the construction of bigger orthogonal Latin hypercubes with desirable properties, using well-known techniques proposed in the literature. Copyright