Efficient and robust block designs for order-of-addition experiments

IF 1.6 3区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Computational Statistics & Data Analysis Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI:10.1016/j.csda.2026.108346
Chang-Yun Lin
{"title":"Efficient and robust block designs for order-of-addition experiments","authors":"Chang-Yun Lin","doi":"10.1016/j.csda.2026.108346","DOIUrl":null,"url":null,"abstract":"<div><div>Designs for Order-of-Addition (OofA) experiments have received growing attention due to their impact on responses based on the sequence of component addition. In certain cases, these experiments involve heterogeneous groups of units, which necessitates the use of blocking to manage variation effects. Despite this, the exploration of block OofA designs remains limited in the literature. As experiments become increasingly complex, addressing this gap is essential to ensure that the designs accurately reflect the effects of the addition sequence and effectively handle the associated variability. Motivated by this, the study seeks to address the gap by expanding the indicator function framework for block OofA designs. The word length pattern is proposed as a criterion for selecting robust block OofA designs. To improve search efficiency and reduce computational demands, an algorithm is developed that employ orthogonal Latin squares for design construction and selection, thereby minimizing the need for exhaustive searches. The analysis, supported by correlation plots, reveals that the algorithms effectively manage confounding and aliasing between effects. Additionally, simulation studies indicate that designs based on the proposed criterion and algorithms achieve power and type I error rates comparable to those of full block OofA designs. This approach offers a practical and efficient method for constructing block OofA designs and may provide valuable insights for future research and applications.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"219 ","pages":"Article 108346"},"PeriodicalIF":1.6000,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167947326000083","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/27 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Designs for Order-of-Addition (OofA) experiments have received growing attention due to their impact on responses based on the sequence of component addition. In certain cases, these experiments involve heterogeneous groups of units, which necessitates the use of blocking to manage variation effects. Despite this, the exploration of block OofA designs remains limited in the literature. As experiments become increasingly complex, addressing this gap is essential to ensure that the designs accurately reflect the effects of the addition sequence and effectively handle the associated variability. Motivated by this, the study seeks to address the gap by expanding the indicator function framework for block OofA designs. The word length pattern is proposed as a criterion for selecting robust block OofA designs. To improve search efficiency and reduce computational demands, an algorithm is developed that employ orthogonal Latin squares for design construction and selection, thereby minimizing the need for exhaustive searches. The analysis, supported by correlation plots, reveals that the algorithms effectively manage confounding and aliasing between effects. Additionally, simulation studies indicate that designs based on the proposed criterion and algorithms achieve power and type I error rates comparable to those of full block OofA designs. This approach offers a practical and efficient method for constructing block OofA designs and may provide valuable insights for future research and applications.
有效和稳健的块设计的顺序加法实验
加法顺序(OofA)实验的设计由于其对基于组分加法顺序的响应的影响而受到越来越多的关注。在某些情况下,这些实验涉及异质单元群,这就需要使用阻塞来管理变异效应。尽管如此,对块OofA设计的探索在文献中仍然有限。随着实验变得越来越复杂,解决这一差距是必不可少的,以确保设计准确地反映了加法序列的影响,并有效地处理相关的可变性。受此启发,本研究试图通过扩展块OofA设计的指标功能框架来解决这一差距。提出了字长模式作为选择稳健块OofA设计的标准。为了提高搜索效率和减少计算量,提出了一种采用正交拉丁方进行设计构造和选择的算法,从而最大限度地减少了穷举搜索的需要。在相关图的支持下,分析表明算法有效地处理了效果之间的混淆和混叠。此外,仿真研究表明,基于所提出的准则和算法的设计实现了与全块OofA设计相当的功率和I型错误率。该方法为构建块OofA设计提供了一种实用而有效的方法,并可能为未来的研究和应用提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis 数学-计算机:跨学科应用
CiteScore
3.70
自引率
5.60%
发文量
167
审稿时长
60 days
期刊介绍: Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书