Space-filling designs and robust orthogonal arrays

实验设计是统计学中的一个研究领域，它处理为科学和技术调查收集数据的有效方法。析因设计是研究一个反应变量和几个解释变量之间关系的一大类实验计划。提出的研究重点是研究两类设计：用于物理实验的正交阵列设计和用于计算机实验的空间填充设计。所有这些设计都可视为因子设计。析因设计的有用性在文献中有充分的记载。他们已经并将继续取得巨大成功的一个主要领域是制造业。提高现有产品的质量或设计新产品都需要使用析因设计。今天，我们看到越来越多的领域成功地使用了析因设计。例如管理、市场营销、金融服务、药物发现和计算机软件测试等等。除如上所述的直接有用外，析因设计还构成了其他有用设计（如响应面方法中的设计）的基础。在更理论的层面上，析因设计的研究是非常重要的，因为它们与组合学和图论有着密切的关系。

Experimental design is an area of study in statistics that deal with efficient methods for collecting data for scientific and technological investigations. Factorial designs are a broad class of experimental plans for studying the relationship between a response variable and several explanatory variables. The proposed research centers on the study of two classes of designs: orthogonal arrays for physical experiments and space-filling designs for computer experiments. All these designs can be regarded as factorial designs. The usefulness of factorial designs is well documented in the literature. One major field where they have found and are continuing to find huge success is the manufacturing industries. Improving the quality of the exsiting products or designing new products all require the use of factorial designs. Today, we are seeing more and more fields in which factorial designs are successfully used. Examples are management, marketing, financial services, drug discovery, and computer software testing, to mention a few. In addition to being directly useful as discussed above, factorial designs form a basis on which other useful designs, such as those in response surface methodology, can be built. At an even more theoretical level, the study of factorial designs is very important because of their intimate relationship with combinatorics and graph theory.