Ordering factorial experiments

IF 3.1 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-04-20 DOI:10.1093/jrsssb/qkad027

Liuqing Yang, Yongdao Zhou, Min-Qian Liu

引用次数: 0

Abstract

In many practical experiments, both the level combinations of factors and the addition orders will affect the responses. However, virtually no construction methods have been provided for such experimental designs. This paper focuses on such experiments, introduces a new type of design called the ordering factorial design, and proposes the nominal main effect component-position model and interaction-main effect component-position model. To obtain efficient fractional designs, we provide some deterministic construction methods. The resulting designs are D-optimal, and the run sizes are much smaller than that of the full designs. Moreover, in some cases, some constructed designs are still D-optimal after reducing the number of components and factors.

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排序阶乘实验

在许多实际实验中，因子的水平组合和加成顺序都会影响响应。然而，实际上没有为这种实验设计提供施工方法。本文针对这类实验，提出了一种新型的排序因子设计，并提出了名义主效应成分-位置模型和交互主效应成分-位置模型。为了获得有效的分式设计，我们提供了一些确定性的构造方法。所得到的设计是d最优的，并且运行尺寸比完整设计的运行尺寸小得多。此外，在某些情况下，一些构建的设计在减少组件和因素的数量后仍然是d最优的。

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 the Royal Statistical Society Series B-Statistical Methodology 数学-统计学与概率论

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.