Abstract–Definitive screening designs permit the study of many quantitative factors in a few runs more than twice the number of factors. In practical applications, researchers often require a design for m quantitative factors, construct a definitive screening design for more than m factors and drop the superfluous columns. This is done when the number of runs in the standard m-factor definitive screening design is considered too limited or when no standard definitive screening design (sDSD) exists for m factors. In these cases, it is common practice to arbitrarily drop the last columns of the larger design. In this article, we show that certain statistical properties of the resulting experimental design depend on the exact columns dropped and that other properties are insensitive to these columns. We perform a complete search for the best sets of 1–8 columns to drop from sDSDs with up to 24 factors. We observed the largest differences in statistical properties when dropping four columns from 8- and 10-factor definitive screening designs. In other cases, the differences are small, or even nonexistent.

中文翻译：

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通过删除列进行最终筛选设计的投影：选择和评估

摘要 – 确定性筛选设计允许在几次运行中研究许多定量因素，其数量是因子数量的两倍。在实际应用中，研究人员通常需要针对 m 个定量因子进行设计，针对 m 个以上的因子构建明确的筛选设计并舍弃多余的色谱柱。当标准 m 因子确定性筛选设计中的运行次数被认为太有限或当 m 因子不存在标准确定性筛选设计 (sDSD) 时，就会执行此操作。在这些情况下，通常的做法是任意删除较大设计的最后一列。在本文中，我们展示了最终实验设计的某些统计属性取决于删除的确切列，而其他属性对这些列不敏感。我们对 1-8 列的最佳集合进行完整搜索，以从 sDSD 中删除最多 24 个因子。当从 8 和 10 因子确定性筛选设计中删除四列时，我们观察到统计特性的最大差异。在其他情况下，差异很小，甚至不存在。