IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v56y2015i1p121-145.html
   My bibliography  Save this article

Analyzing supersaturated designs for discrete responses via generalized linear models

Author

Listed:
  • N. Balakrishnan
  • C. Koukouvinos
  • C. Parpoula

Abstract

A supersaturated design is a factorial design in which the number of factors to be estimated is larger than the available number of experimental runs. The cost and time required for many industrial experimentations can be reduced by using the class of supersaturated designs, since the main goal for such a design is to identify only a few of the factors under consideration that have dominant effects and to do this identification at a minimal cost. While most of the literature on supersaturated designs has focused on the construction of designs and their optimality properties, the data analysis of such designs has not been developed to a great extent. In this paper, we propose a supersaturated design analysis method, by assuming generalized linear models for discrete responses, for analyzing main effects designs and identifying simultaneously the effects that are significant. Empirical study demonstrates that this method performs well with low Type I and Type II error rates. The proposed method is therefore useful as it enables us to use supersaturated designs for analyzing data on discrete response regression models. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:1:p:121-145
    DOI: 10.1007/s00362-013-0569-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-013-0569-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-013-0569-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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.
    2. Hans Pettersson, 2005. "Optimal design in average for inference in generalized linear models," Statistical Papers, Springer, vol. 46(1), pages 79-99, January.
    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. 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.
    5. Chong Hong & Beom Kim, 2011. "Mutual information and redundancy for categorical data," Statistical Papers, Springer, vol. 52(1), pages 17-31, February.
    6. Claudia Czado & Adrian Raftery, 2006. "Choosing the link function and accounting for link uncertainty in generalized linear models using Bayes factors," Statistical Papers, Springer, vol. 47(3), pages 419-442, June.
    Full references (including those not matched with items on IDEAS)

    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. Das, Ujjwal & Gupta, Sudhir & Gupta, Shuva, 2014. "Screening active factors in supersaturated designs," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 223-232.
    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. Singh, Rakhi & Stufken, John, 2024. "Factor selection in screening experiments by aggregation over random models," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    4. Ernest Fokoue & Bertrand Clarke, 2011. "Bias-variance trade-off for prequential model list selection," Statistical Papers, Springer, vol. 52(4), pages 813-833, November.
    5. Diego Ramos Canterle & Fábio Mariano Bayer, 2019. "Variable dispersion beta regressions with parametric link functions," Statistical Papers, Springer, vol. 60(5), pages 1541-1567, October.
    6. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
    7. VÁZQUEZ-ALCOCER, Alan & SCHOEN, Eric D. & GOOS, Peter, 2018. "A mixed integer optimization approach for model selection in screening experiments," Working Papers 2018007, University of Antwerp, Faculty of Business and Economics.
    8. Yiyun Shou & Michael Smithson, 2015. "Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 236-256, March.
    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. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.
    11. Bhat, Chandra R. & Astroza, Sebastian & Hamdi, Amin S., 2017. "A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 126-148.
    12. Adam Slez, 2019. "The Difference Between Instability and Uncertainty: Comment on Young and Holsteen (2017)," Sociological Methods & Research, , vol. 48(2), pages 400-430, May.
    13. Peter Congdon, 2022. "A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    14. 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.
    15. 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.
    16. Bradley Jones & Dibyen Majumdar, 2014. "Optimal Supersaturated Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1592-1600, December.
    17. 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.
    18. Singh, Satya Prakash & Mukhopadhyay, Siuli, 2016. "Bayesian crossover designs for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 35-50.
    19. Assaf, Ata & Charif, Husni & Demir, Ender, 2022. "Information sharing among cryptocurrencies: Evidence from mutual information and approximate entropy during COVID-19," Finance Research Letters, Elsevier, vol. 47(PA).
    20. 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.

    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:spr:stpapr:v:56:y:2015:i:1:p:121-145. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.