D- And A- Optimal Orthogonally Blocked Mixture Component-Amount Designs via Projections

Bushra Husain, Afrah Hafeez
{"title":"D- And A- Optimal Orthogonally Blocked Mixture Component-Amount Designs via Projections","authors":"Bushra Husain, Afrah Hafeez","doi":"10.19139/soic-2310-5070-591","DOIUrl":null,"url":null,"abstract":"Mixture experiments are usually designed to study the effects on the response by changing the relative proportions of the mixture ingredients. This is usually achieved by keeping the total amount fixed but in many practical applications such as medicine or biology, not only are the proportions of mixture ingredients involved but also their total amount is of particular interest. Such experiments are called mixture amount experiments. In such experiments, the usual constraint on the mixture proportions that they should sum to unity is relaxed. The optimality of the design strictly depends on the nature of the underlying model. In this paper, we have obtained D- and A- optimal orthogonally blocked mixture component-amount designs in two and three ingredients via projections based on the reduced cubic canonical model presented by Husain and Sharma [7] and the additive quadratic mixture model proposed by Husain and Parveen [3], respectively.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics, Optimization & Information Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.19139/soic-2310-5070-591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Mixture experiments are usually designed to study the effects on the response by changing the relative proportions of the mixture ingredients. This is usually achieved by keeping the total amount fixed but in many practical applications such as medicine or biology, not only are the proportions of mixture ingredients involved but also their total amount is of particular interest. Such experiments are called mixture amount experiments. In such experiments, the usual constraint on the mixture proportions that they should sum to unity is relaxed. The optimality of the design strictly depends on the nature of the underlying model. In this paper, we have obtained D- and A- optimal orthogonally blocked mixture component-amount designs in two and three ingredients via projections based on the reduced cubic canonical model presented by Husain and Sharma [7] and the additive quadratic mixture model proposed by Husain and Parveen [3], respectively.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
D-和A-最优正交阻塞混合成分量的投影设计
通常设计混合实验来研究通过改变混合成分的相对比例对响应的影响。这通常是通过保持总量固定来实现的,但在许多实际应用中,如医学或生物学,不仅涉及混合成分的比例,而且还涉及它们的总量。这样的实验称为混合物量实验。在这样的实验中,通常对混合比例的限制,即它们应该之和为一,被放宽了。设计的最优性严格依赖于底层模型的性质。本文分别基于Husain和Sharma[7]提出的简化三次正则模型和Husain和Parveen[3]提出的可加二次混合模型,通过投影得到了两种和三种原料正交阻塞混合组分量的D-和A-最优设计。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions Bayesian and Non-Bayesian Estimation for The Parameter of Inverted Topp-Leone Distribution Based on Progressive Type I Censoring Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets An Algorithm for Solving Quadratic Programming Problems with an M-matrix An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1