A cluster analysis selection strategy for supersaturated designs

Authors:
Peng Li;Shengli Zhao;Runchu Zhang
Affiliations:
School of Mathematical Sciences, Capital Normal University, Beijing 100037, China and LPMC and School of Mathematical Sciences, Nankai University, Tianjin 300071, China;School of Mathematical Sciences, Qufu Normal University, Qufu 273165, China;KLAS and School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China and LPMC and School of Mathematical Sciences, Nankai University, Tianjin 300071, China
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 4
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Abstract

Supersaturated designs (SSDs) are widely researched because they can greatly reduce the number of experiments. However, analyzing the data from SSDs is not easy as their run size is not large enough to estimate all the main effects. This paper introduces contrast-orthogonality cluster and anticontrast-orthogonality cluster to reflect the inner structure of SSDs which are helpful for experimenters to arrange factors to the columns of SSDs. A new strategy for screening active factors is proposed and named as contrast-orthogonality cluster analysis (COCA) method. Simulation studies demonstrate that this method performs well compared to most of the existing methods. Furthermore, the COCA method has lower type II errors and it is easy to be understood and implemented.