Optimal s-level fractional factorial designs under baseline parameterization

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference Pub Date : 2024-09-27 DOI:10.1016/j.jspi.2024.106242

Zhaohui Yan, Shengli Zhao

引用次数: 0

Abstract

In this paper, we explore the minimum aberration criterion for

s

-level designs under baseline parameterization, called BP-MA. We give a complete search method and an incomplete search method to obtain the BP-MA (or nearly BP-MA) designs. The methodology has no restriction on

s

, the levels of the factors. The catalogues of (nearly) BP-MA designs with

s = 2, 3, 4, 5

levels are provided.

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基线参数化条件下的最优 s 级分数因子设计

本文探讨了基线参数化条件下 s 级设计的最小畸变准则，称为 BP-MA。我们给出了一种完全搜索方法和一种不完全搜索方法来获得 BP-MA（或近似 BP-MA）设计。该方法对因子水平 s 没有限制。我们提供了 s=2,3,4,5 级的（近似）BP-MA 设计目录。

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

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

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.