构建 2fi- 最佳行列设计

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Yingnan Zhang, Jiangmin Pan, Lei Shi
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引用次数: 0

摘要

能对所有主效应和最大数量的双因素交互作用(2fi)进行无约束估计的行列式设计被称为 2fi 最佳设计。最近,这一问题因其在工业或物理实验中的广泛应用而备受关注。已有人提出了 2fi-optimal 两级和三级全因子和分数因子行列式设计的构造。但是,对于更高的素数级,目前还没有结果。在本文中,我们给出了针对任意奇数素数级 s 和任意参数组合的 2fi-optimal sn 全因子行列式设计的理论构造,以及针对任意素数级 s 和任意参数组合的 2fi-optimal sn-1 小数因子行列式设计的理论构造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of 2fi-optimal row–column designs

Row–column designs that provide unconfounded estimation of all main effects and the maximum number of two-factor interactions (2fi’s) are called 2fi-optimal. This issue has been paid great attention recently for its wide application in industrial or physical experiments. The constructions of 2fi-optimal two-level and three-level full factorial and fractional factorial row–column designs have been proposed. However, the results for higher prime levels have not been achieved yet. In this paper, we give theoretical constructions of 2fi-optimal sn full factorial row–column designs for any odd prime level s and any parameter combination, and theoretical constructions of 2fi-optimal sn1 fractional factorial row–column designs for any prime level s and any parameter combination.

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来源期刊
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference 数学-统计学与概率论
CiteScore
2.10
自引率
11.10%
发文量
78
审稿时长
3-6 weeks
期刊介绍: The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
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