范畴变量
实验设计
混叠
正交数组
计算机科学
班级(哲学)
主要影响
数学
数学优化
统计
机器学习
人工智能
田口方法
欠采样
作者
Bradley Jones,Christopher J. Nachtsheim
标识
DOI:10.1080/00224065.2013.11917921
摘要
Recently, Jones and Nachtsheim (2011) proposed a new class of designs called definitive screening designs (DSDs). These designs have three levels, provide estimates of main effects that are unbiased by any second-order effect, require only one more than twice as many runs as there are factors, and avoid confounding of any pair of second-order effects. For designs having six factors or more, these designs project to efficient response surface designs with three or fewer factors. A limitation of these designs is that all factors must be quantitative. In this paper, we develop column-augmented DSDs that can accommodate any number of two-level qualitative factors using two methods. The DSD-augment method provides highly efficient designs that are still definitive in the sense that the estimates of all main effects continue to be unbiased by any active second-order effects. An alternative procedure, the ORTH-augment approach, leads to designs that are orthogonal linear main effects plans; however, some partial aliasing between main effects and interactions involving the categorical factors is present.
科研通智能强力驱动
Strongly Powered by AbleSci AI