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A Method for Augmenting Supersaturated Designs with Newly Added Factors

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  • Chun-Wei Zheng

    (State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
    School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China
    These authors contributed equally to this work.)

  • Zong-Feng Qi

    (State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
    These authors contributed equally to this work.)

  • Qiao-Zhen Zhang

    (School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China
    These authors contributed equally to this work.)

  • Min-Qian Liu

    (School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China)

Abstract

Follow-up experimental designs are popularly used in industry. In practice, some important factors may be neglected for various reasons in the first-stage experiment and they need to be added in the next stage. In this paper, we propose a method for augmenting supersaturated designs with newly added factors and augmented levels using the Bayesian D -optimality criterion. In addition, we suggest using the integrated Bayesian D -optimal augmented design to plan the follow-up experiment when the newly added factors have been allowed to vary in an appropriate region. Examples and simulation results show that the augmented designs perform well in improving identified rates of latent factor effects.

Suggested Citation

  • Chun-Wei Zheng & Zong-Feng Qi & Qiao-Zhen Zhang & Min-Qian Liu, 2022. "A Method for Augmenting Supersaturated Designs with Newly Added Factors," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:60-:d:1013447
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    References listed on IDEAS

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    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. Jiaqi Liu & Zujun Ou & Liuping Hu & Kang Wang, 2019. "Lee discrepancy on mixed two- and three-level uniform augmented designs," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(10), pages 2409-2424, May.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. 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.
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