Follow-up designs to resolve confounding in multifactor experiments
Follow-up designs to resolve confounding in multifactor experiments
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DOI：
10.2307/1271297
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发表时间：
1996-11-01
期刊：
影响因子：
2.5
通讯作者：
Box, G
中科院分区：
文献类型：
--
作者：
Meyer, RD;Steinberg, DM;Box, G
由于因子稀疏性，部分阶乘、Plackett-Burman 和其他多因子设计在实践中通常是有效的。也就是说，所研究的众多因素中只有少数几个因素会产生重大影响。在那些活跃因素中，这些设计可以具有高分辨率。我们之前开发了一种基于模型判别思想的贝叶斯方法，可以揭示活跃因素。有时，由于可能的影响之间的混杂，部分实验的结果是不明确的，并且多个模型可能与贝叶斯构造中的数据一致，我们开发了一种设计后续实验的方法来解决这种不明确性。这个想法是选择允许最大程度区分合理模型的运行。该方法比代数解耦混叠交互的方法更通用，并且比需要指定单个模型的最优设计方法更合适。该方法通过部分实验的例子进行说明。
Fractional factorial, Plackett-Burman, and other multifactor designs are often effective in practice due to factor sparsity. That is, just a few of the many factors studied will have major effects. In those active factors, these designs can have high resolution. We have previously developed a Bayesian method based on the idea of model discrimination that uncovers the active factors. Sometimes, the results of a fractional experiment are ambiguous due to confounding among the possible effects, and more than one model may be consistent with the data Within the Bayesian construct, we have developed a method for designing a follow-up experiment to resolve this ambiguity. The idea is to choose runs that allow maximum discrimination among the plausible models. This method is more general than methods that algebraically decouple aliased interactions and more appropriate than optimal design methods that require specification of a single model. The method is illustrated through examples of fractional experiments.