分式析因设计
栏(排版)
数学
析因实验
行和列空间
素数(序理论)
优化设计
Plackett–伯曼设计
复制
阶乘
数学优化
统计
组合数学
排
计算机科学
几何学
数学分析
响应面法
连接(主束)
数据库
作者
Zheng Zhou,Yixiao Zhou
出处
期刊:Biometrika
[Oxford University Press]
日期:2022-08-10
卷期号:110 (2): 537-549
标识
DOI:10.1093/biomet/asac046
摘要
Summary Row-column designs have been widely used in experiments involving double confounding. Among them, one that provides unconfounded estimation of all main effects and as many two-factor interactions as possible is preferred, and is called optimal. Most current work focuses on the construction of two-level row-column designs, while the corresponding optimality theory has been largely ignored. Moreover, most constructed designs contain at least one replicate of a full factorial design, which is not flexible as the number of factors increases. In this study, a theoretical framework is built up to evaluate the optimality of row-column designs with prime level. A method for constructing optimal row-column designs with prime level is proposed. Subsequently, optimal full factorial three-level row-column designs are constructed for any parameter combination. Optimal fractional factorial two-level and three-level row-column designs are also constructed for cost saving.
科研通智能强力驱动
Strongly Powered by AbleSci AI