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Augmenting supersaturated designs with Bayesian D-optimality

Author

Listed:
  • Gutman, Alex J.
  • White, Edward D.
  • Lin, Dennis K.J.
  • Hill, Raymond R.

Abstract

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:1147-1158
    DOI: 10.1016/j.csda.2013.09.009
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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, 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.
    3. Marley, Christopher J. & Woods, David C., 2010. "A comparison of design and model selection methods for supersaturated experiments," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3158-3167, December.
    4. Ruggoo, Arvind & Vandebroek, Martina, 2004. "Bayesian sequential - optimal model-robust designs," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 655-673, November.
    5. Edwards, David J. & Mee, Robert W., 2011. "Supersaturated designs: Are our results significant?," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2652-2664, September.
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