标题： A-ComVar：公共方差设计的灵活扩展 显示英文标题

标题： A-ComVar: A Flexible Extension of Common Variance Design

摘要： 我们考虑一类线性模型的非正则分数因子实验。 这些模型有一个共同的总体均值和主要效应，但它们可能有不同的两因子交互作用。 这里我们假设为了简化，三因子及更高阶的交互作用可以忽略不计。 在没有先验知识的情况下，哪些交互作用是重要的，选择一个结果所有交互作用估计方差相等的设计来帮助模型区分是合理的。 这种设计称为公共方差设计，在不进行所有可能设计的穷举搜索的情况下，识别它们可能会很具挑战性。 在这项工作中，我们引入了一种称为近似公共方差或 A-ComVar 设计的公共方差设计的扩展。 我们开发了一种数值方法来寻找 A-ComVar 设计，这种方法比穷举搜索高效得多。 我们介绍了对于不同数量的因素、运行次数和交互作用可以找到的 A-ComVar 设计类型。 我们进一步通过与其他文献中流行的 designs 的比较，展示了公共方差和 A-ComVar designs 的竞争性能。 显示英文摘要

摘要： We consider nonregular fractions of factorial experiments for a class of linear models. These models have a common general mean and main effects, however they may have different 2-factor interactions. Here we assume for simplicity that 3-factor and higher order interactions are negligible. In the absence of a priori knowledge about which interactions are important, it is reasonable to prefer a design that results in equal variance for the estimates of all interaction effects to aid in model discrimination. Such designs are called common variance designs and can be quite challenging to identify without performing an exhaustive search of possible designs. In this work, we introduce an extension of common variance designs called approximate common variance, or A-ComVar designs. We develop a numerical approach to finding A-ComVar designs that is much more efficient than an exhaustive search. We present the types of A-ComVar designs that can be found for different number of factors, runs, and interactions. We further demonstrate the competitive performance of both common variance and A-ComVar designs using several comparisons to other popular designs in the literature.