The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of this first stage is influenced by three components: (1) main effect estimators' variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this paper, we propose a design criterion based on expected confidence intervals of the first stage analysis that balances all three components. To address model misspecification, we propose a computationally-efficient all-subsets analysis and a corresponding constrained design criterion based on lack-of-fit. Scenarios found in existing design literature are revisited with our criteria and new designs are provided that improve upon existing methods.

翻译：筛选实验的分析通常分为两个阶段进行，首先在主效应模型下通过分析进行因子筛选。第一阶段的成功受三个因素影响：(1) 主效应估计量的方差，(2) 偏差，以及 (3) 噪声方差的估计。其中因素 (3) 直至近期才通过确保噪声方差无偏估计的设计技术得到关注。本文提出了一种基于第一阶段分析期望置信区间的设计准则，该准则平衡了上述三个因素。针对模型设定错误问题，我们提出了一种计算高效的全体子集分析及相应的基于失拟的约束设计准则。利用我们的准则重新审视了现有设计文献中的典型场景，并提供了优于现有方法的新设计。