高强度正交阵列示意图

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
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引用次数: 0

摘要

示意正交阵列与关联方案密切相关。而哪些正交阵列属于示意正交阵列以及如何对它们进行分类是 Hedayat 等人(1999 年)提出的一个未决问题。通过使用汉明距离,本文提出了一些构建高强度示意对称阵列和混合正交阵列的一般方法。作为这些方法的应用,我们构建了关联方案和许多新的示意正交阵列,包括此类阵列的几个无限类。本文提供了一些示例来说明构建方法。本文给出了 Hedayat 等人(1999 年)提出的高强度对称和混合正交阵列问题的部分解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On schematic orthogonal arrays of high strength

Schematic orthogonal arrays are closely related to association schemes. And which orthogonal arrays are schematic orthogonal arrays and how to classify them is an open problem proposed by Hedayat et al. (1999). By using the Hamming distances, this paper presents some general methods for constructing schematic symmetric and mixed orthogonal arrays of high strength. As applications of these methods, we construct association schemes and many new schematic orthogonal arrays including several infinite classes of such arrays. Some examples are provided to illustrate the construction methods. The paper gives the partial solution of the problem by Hedayat et al. (1999) for symmetric and mixed orthogonal arrays of high strength.

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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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