Construction of three-level factorial designs with general minimum lower-order confounding via resolution IV designs

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Metrika Pub Date : 2024-09-09 DOI:10.1007/s00184-024-00972-2

Tian-fang Zhang, Yingxing Duan, Shengli Zhao, Zhiming Li

引用次数: 0

Abstract

The general minimum lower order confounding (GMC) is a criterion for selecting designs when the experimenter has prior information about the order of the importance of the factors. The paper considers the construction of \(3^{n-m}\) designs under the GMC criterion. Based on some theoretical results, it proves that some large GMC \(3^{n-m}\) designs can be obtained by combining some small resolution IV designs T. All the results for \(4\le \#\{T\} \le 20\) are tabulated in the table, where \(\#\) means the cardinality of a set.

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通过解析 IV 设计构建具有一般最小低阶混杂性的三级因子设计

一般最小低阶混杂（GMC）是当实验者拥有关于各因素重要性顺序的先验信息时选择设计的标准。本文考虑了在 GMC 标准下构建 \(3^{n-m}\) 设计。基于一些理论结果，它证明了一些大的 GMC （3^{n-m}\）设计可以通过组合一些小的分辨率 IV 设计 T 而得到。所有关于 \(4\le \#\{T\} \le 20\) 的结果都列在表中，其中 \(\#\) 表示集合的卡入度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Metrika 数学-统计学与概率论

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.