使用Hadamard矩阵构建混合水平筛选设计

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Journal of Statistical Planning and Inference Pub Date : 2023-11-23 DOI:10.1016/j.jspi.2023.106131
Bo Hu , Dongying Wang , Fasheng Sun
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引用次数: 0

摘要

现代实验通常涉及大量的变量。筛选设计允许实验人员在最少数量的试验中确定积极因素。为节省成本,筛选实验只考虑低水平因子设计,特别是二水平和三水平设计。在本文中,我们提供了一种系统的方法来构建包含两层和三层因素的筛选设计,该设计基于具有折叠结构的Hadamard矩阵。所提出的设计在d-最优和a -最优准则方面具有良好的性能,并且主效应的估计不受二阶效应的偏倚,使其非常适合筛选实验。此外,还得到了D-最优性和a -最优性的理论结果。
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Construction of mixed-level screening designs using Hadamard matrices

Modern experiments typically involve a very large number of variables. Screening designs allow experimenters to identify active factors in a minimum number of trials. To save costs, only low-level factorial designs are considered for screening experiments, especially two- and three-level designs. In this article, we provide a systematic method to construct screening designs that contain both two- and three-level factors based on Hadamard matrices with the fold-over structure. The proposed designs have good performance in terms of D-optimal and A-optimal criteria, and the estimates of the main effects are unbiased by the second-order effects, making them very suitable for screening experiments. Besides, some theoretical results on D- and A-optimality are obtained as a by-product.

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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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