Individual Word Length Patterns for Fractional Factorial(Split-Plot) Designs

被引:0
作者
HAN Xiaoxue [1 ]
CHEN Jianbin [2 ]
YANG Jianfeng [3 ]
LIU Minqian [3 ]
机构
[1] School of Statistics and Data Science, Qufu Normal University
[2] School of Mathematics and Statistics, Beijing Institute of Technology
[3] School of Statistics and Data Science, LPMC & KLMDASR, Nankai University
关键词
D O I
暂无
中图分类号
O212.6 [试验分析与试验设计];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Fractional factorial(FF) designs are commonly used for factorial experiments in many fields. When some prior knowledge has shown that some factors are more likely to be significant than others, Li, et al.(2015) proposed a new pattern, called the individual word length pattern(IWLP), which, defined on a column of the design matrix, measures the aliasing of the effect assigned to this column and effects involving other factors. In this paper, the authors first investigate the relationships between the IWLP and other popular criteria for regular FF designs. As we know,fractional factorial split-plot(FFSP) designs are important both in theory and practice. So another contribution of this paper is extending the IWLP criterion from FF designs to FFSP designs. The authors propose the IWLP of a factor from the whole-plot(WP), or sub-plot(SP), denoted by the Iw WLP and Is WLP respectively, in the FFSP design. The authors further propose combined word length patterns Cw WLP and Cs WLP, in order to select good designs for different cases. The new criteria Cw WLP and Cs WLP apply to the situations that the potential important factors are in WP or SP, respectively. Some examples are presented to illustrate the selected designs based on the criteria established here.
引用
收藏
页码:2082 / 2099
页数:18
相关论文
共 27 条
[1]  
WEI JiaLin 1,4,YANG JianFeng 1,LI Peng 3 & ZHANG RunChu 1,2,1 School of Mathematical Sciences and LPMC,Nankai University,Tianjin 300071,China,2 KLAS and School of Mathematics and Statistics,Northeast Normal University,Changchun 130024,China,3 School of Mathematical Sciences,Capital Normal University,Beijing 100037,China,4 School of Sciences,Tianjin University of Commerce,Tianjin 300134,China.Split-plot designs with general minimum lower-order confounding[J].Science China(Mathematics),2010(04):93
[2]  
Wu C. F. Jeff,Hamada Michael.Experiments:Planning, Analysis, and Optimization[J].
[3]  
Xiaoxue Han,Min-Qian Liu,Jian-Feng Yang,Shengli Zhao.Mixed 2- and 2 r -level fractional factorial split-plot designs with clear effects[J].Journal of Statistical Planning and Inference,2020
[4]  
Xiaoxue Han,Jianbin Chen,Min-Qian Liu,Shengli Zhao.Asymmetrical split-plot designs with clear effects[J].Metrika,2019
[5]  
William Li,Robert W Mee,Qi Zhou.Using Individual Factor Information in Fractional Factorial Designs[J].Technometrics,2019
[6]   Mixed-level designs with resolution III or IV containing clear two-factor interaction components [J].
Qianqian Zhao ;
Shengli Zhao .
Metrika, 2015, 78 :953-965
[7]  
Sartono, Bagus,Goos, Peter,Schoen, Eric.Constructing General Orthogonal Fractional Factorial Split-Plot Designs[J].Technometrics,2015
[8]  
William Li,Qi Zhou,Runchu Zhang.Effective designs based on individual word length patterns[J].Journal of Statistical Planning and Inference,2015
[9]  
Xue-Min Zi,Runchu Zhang,Min-Qian Liu.On optimal two-level nonregular factorial split-plot designs[J].Journal of Complexity,2012
[10]  
Shengli Zhao,Xiangfei Chen.Mixed two- and four-level fractional factorial split-plot designs with clear effects[J].Journal of Statistical Planning and Inference,2012