IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i6p1605-1612.html
   My bibliography  Save this article

A cluster analysis selection strategy for supersaturated designs

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00025-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Georgiou, Stelios D., 2008. "Modelling by supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 428-435, December.
    2. 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.
    3. Li, Runze & Lin, Dennis K. J., 2002. "Data analysis in supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 135-144, September.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nicola Rossi & Mario Bačić & Lovorka Librić & Meho Saša Kovačević, 2023. "Methodology for Identification of the Key Levee Parameters for Limit-State Analyses Based on Sequential Bifurcation," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    3. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    4. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    5. Wang, S. & Huang, G.H., 2015. "A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management," European Journal of Operational Research, Elsevier, vol. 240(2), pages 572-581.
    6. Das, Ujjwal & Gupta, Sudhir & Gupta, Shuva, 2014. "Screening active factors in supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 223-232.
    7. Michael Rosenblum & Ethan X. Fang & Han Liu, 2020. "Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 749-772, July.
    8. 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.
    9. 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.
    10. Stamatis Choudalakis & Marilena Mitrouli & Athanasios Polychronou & Paraskevi Roupa, 2021. "Solving High-Dimensional Problems in Statistical Modelling: A Comparative Study," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    11. N. Balakrishnan & C. Koukouvinos & C. Parpoula, 2015. "Analyzing supersaturated designs for discrete responses via generalized linear models," Statistical Papers, Springer, vol. 56(1), pages 121-145, February.
    12. Kleijnen, J.P.C., 2007. "Screening Experiments for Simulation : A Review," Discussion Paper 2007-21, Tilburg University, Center for Economic Research.
    13. Emanuele Borgonovo & Elmar Plischke & Giovanni Rabitti, 2022. "Interactions and computer experiments," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1274-1303, September.
    14. Yamada, Shu & Matsui, Michiyo & Matsui, Tomomi & Lin, Dennis K.J. & Takahashi, Takenori, 2006. "A general construction method for mixed-level supersaturated design," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 254-265, January.
    15. Hong Wan & Bruce E. Ankenman & Barry L. Nelson, 2006. "Controlled Sequential Bifurcation: A New Factor-Screening Method for Discrete-Event Simulation," Operations Research, INFORMS, vol. 54(4), pages 743-755, August.
    16. 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.
    17. Last, Michael & Luta, Gheorghe & Orso, Alex & Porter, Adam & Young, Stan, 2008. "Pooled ANOVA," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5215-5228, August.
    18. Georgiou, Stelios D., 2008. "Modelling by supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 428-435, December.
    19. N. Balakrishnan & C. Koukouvinos & C. Parpoula, 2013. "An information theoretical algorithm for analyzing supersaturated designs for a binary response," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 1-18, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1605-1612. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.