ANALYSIS OF DESIGNED EXPERIMENTS WITH COMPLEX ALIASING
ANALYSIS OF DESIGNED EXPERIMENTS WITH COMPLEX ALIASING
复制标题
DOI：
10.1080/00224065.1992.11979383
复制
发表时间：
1992-07-01
影响因子：
2.5
通讯作者：
WU, CFJ
中科院分区：
文献类型：
--
作者：
HAMADA, M;WU, CFJ
传统上，Plackett-Burman（PB）设计已被用于筛选实验，以确定重要的主效应。PB设计的运行大小不是2的幂，因为其复杂的混叠模式而受到批评，根据传统智慧，这会产生混乱的结果。本文超越了传统的方法，提出了一种分析策略，娱乐互动，除了主效应。基于效应稀疏性和效应遗传性的概念，所提出的过程利用了设计的复杂别名模式，从而将其“责任”转化为优势。三个真实的实验的程序演示显示了提取重要信息的数据，到目前为止，被错过了潜力。一些限制进行了讨论，并扩展，以克服它们。建议的程序也适用于更一般的混合水平设计，已变得越来越流行。
Traditionally, Plackett-Burman (PB) designs have been used in screening experiments for identifying important main effects. The PB designs whose run sizes are not a power of two hove been criticized for their complex aliasing patterns, which according to conventional wisdom gives confusing results. This paper goes beyond the traditional approach by proposing an analysis strategy that entertains interactions in addition to main effects. Based on the precepts of effect sparsity and effect heredity, the proposed procedure exploits the designs' complex aliasing patterns, thereby turning their "liability" into an advantage. Demonstration of the procedure on three real experiments shows the potential for extracting important information available in the data that has, until now, been missed. Some limitations are discussed, and extensions to overcome them are given. The proposed procedure also applies to more general mixed level designs that have become increasingly popular.