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A cluster analysis selection strategy for supersaturated designs

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  • Li, Peng
  • Zhao, Shengli
  • Zhang, Runchu

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.

Suggested Citation

  • Li, Peng & Zhao, Shengli & Zhang, Runchu, 2010. "A cluster analysis selection strategy for supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1605-1612, June.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1605-1612
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    References listed on IDEAS

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    1. Georgiou, Stelios D., 2008. "Modelling by supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 428-435, December.
    2. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
    3. Sarkar, Angshuman & Lin, Dennis K.J. & Chatterjee, Kashinath, 2009. "Probability of correct model identification in supersaturated design," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1224-1230, May.
    4. Dean, A. M. & Lewis, S. M., 2002. "Comparison of group screening strategies for factorial experiments," Computational Statistics & Data Analysis, Elsevier, vol. 39(3), pages 287-297, May.
    5. S. M. Lewis & A. M. Dean, 2001. "Detection of interactions in experiments on large numbers of factors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 633-672.
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    1. Huang, Hengzhen & Yang, Jinyu & Liu, Min-Qian, 2014. "Functionally induced priors for componentwise Gibbs sampler in the analysis of supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 1-12.
    2. Gutman, Alex J. & White, Edward D. & Lin, Dennis K.J. & Hill, Raymond R., 2014. "Augmenting supersaturated designs with Bayesian D-optimality," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1147-1158.

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