IDEAS home Printed from https://ideas.repec.org/p/ant/wpaper/2013027.html
   My bibliography  Save this paper

Staggered-level designs for response surface modeling

Author

Listed:
  • ARNOUTS, Heidi
  • GOOS, Peter

Abstract

In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time restrictions. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments involving two hard-to-change factors. The staggered-level design allows both hard-to-change factors to be reset at different points in time, resulting in a typical staggering pattern of factor level resettings. It has been shown that, for two-level designs, this staggering pattern leads to statistical benefits in comparison to the split-plot and the split-split-plot design. In this paper, we investigate whether the benefits of the staggered-level design carry over to situations where the objective is to optimize a response, and where a second-order response surface model is in place. To this end, we study several examples of D-and I-optimal staggered-level response surface designs.

Suggested Citation

  • ARNOUTS, Heidi & GOOS, Peter, 2013. "Staggered-level designs for response surface modeling," Working Papers 2013027, University of Antwerp, Faculty of Business and Economics.
    • Handle: RePEc:ant:wpaper:2013027
    as

    Download full text from publisher

    File URL: https://repository.uantwerpen.be/docman/irua/67f839/1781b5f7.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. MACHARIA, Harrison & GOOS, Peter, 2010. "D-optimal and D-efficient equivalent-estimation second-order split-plot designs," Working Papers 2010011, University of Antwerp, Faculty of Business and Economics.
    2. Eric Schoen, 1999. "Designing fractional two-level experiments with nested error structures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 495-508.
    3. Bradley Jones & Peter Goos, 2009. "D-optimal design of split-split-plot experiments," Biometrika, Biometrika Trust, vol. 96(1), pages 67-82.
    4. ARNOUTS, Heidi & GOOS, Peter, 2008. "Staggered designs for experiments with more than one hard-to-change factor," Working Papers 2008018, University of Antwerp, Faculty of Business and Economics.
    5. JONES, Bradley & GOOS, Peter, 2012. "I-optimal versus D-optimal split-plot response surface designs," Working Papers 2012002, University of Antwerp, Faculty of Business and Economics.
    6. Bradley Jones & Peter Goos, 2007. "A candidate‐set‐free algorithm for generating D‐optimal split‐plot designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(3), pages 347-364, May.
    7. D. R. Bingham & E. D. Schoen & R. R. Sitter, 2004. "Designing fractional factorial split‐plot experiments with few whole‐plot factors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 325-339, April.
    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. Palhazi Cuervo, Daniel & Goos, Peter & Sörensen, Kenneth, 2017. "An algorithmic framework for generating optimal two-stratum experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 224-249.

    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. Bradley Jones & Peter Goos, 2009. "D-optimal design of split-split-plot experiments," Biometrika, Biometrika Trust, vol. 96(1), pages 67-82.
    2. Borrotti, Matteo & Sambo, Francesco & Mylona, Kalliopi, 2023. "Multi-objective optimisation of split-plot designs," Econometrics and Statistics, Elsevier, vol. 28(C), pages 163-172.
    3. ARNOUTS, Heidi & GOOS, Peter, 2009. "Design and analysis of industrial strip-plot experiments," Working Papers 2009007, University of Antwerp, Faculty of Business and Economics.
    4. Sambo, Francesco & Borrotti, Matteo & Mylona, Kalliopi, 2014. "A coordinate-exchange two-phase local search algorithm for the D- and I-optimal designs of split-plot experiments," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1193-1207.
    5. SCHOEN, Eric D. & JONES, Bradley & GOOS, Peter, 2010. "Split-plot experiments with factor-dependent whole-plot sizes," Working Papers 2010001, University of Antwerp, Faculty of Business and Economics.
    6. Palhazi Cuervo, Daniel & Goos, Peter & Sörensen, Kenneth, 2017. "An algorithmic framework for generating optimal two-stratum experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 224-249.
    7. JONES, Bradley & GOOS, Peter, 2012. "I-optimal versus D-optimal split-plot response surface designs," Working Papers 2012002, University of Antwerp, Faculty of Business and Economics.
    8. Smucker, Byran J. & Castillo, Enrique del & Rosenberger, James L., 2012. "Model-robust designs for split-plot experiments," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4111-4121.
    9. Kalliopi Mylona & Harrison Macharia & Peter Goos, 2013. "Three-level equivalent-estimation split-plot designs based on subset and supplementary difference set designs," IISE Transactions, Taylor & Francis Journals, vol. 45(11), pages 1153-1165.
    10. Murat Kulahci & John Tyssedal, 2017. "Split-plot designs for multistage experimentation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(3), pages 493-510, February.
    11. Xiaodong Li & Xu He & Yuanzhen He & Hui Zhang & Zhong Zhang & Dennis K. J. Lin, 2017. "The Design and Analysis for the Icing Wind Tunnel Experiment of a New Deicing Coating," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1417-1429, October.
    12. GOOS, Peter & JONES, Bradley & SYAFITRI, Utami, 2013. "I-optimal mixture designs," Working Papers 2013033, University of Antwerp, Faculty of Business and Economics.
    13. K. Chatterjee & C. Koukouvinos & K. Mylona, 2020. "Construction of supersaturated split-plot designs," Statistical Papers, Springer, vol. 61(5), pages 2203-2219, October.
    14. Xiaoxue Han & Jianbin Chen & Min-Qian Liu & Shengli Zhao, 2020. "Asymmetrical split-plot designs with clear effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 779-798, October.
    15. Arnouts, Heidi & Goos, Peter, 2010. "Update formulas for split-plot and block designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3381-3391, December.
    16. Moein Saleh & Ming-Hung Kao & Rong Pan, 2017. "Design D-optimal event-related functional magnetic resonance imaging experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 73-91, January.
    17. K. Chatterjee & C. Koukouvinos, 2021. "Construction of mixed-level supersaturated split-plot designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(7), pages 949-967, October.
    18. Marcin Dutka & Mario Ditaranto & Terese Løvås, 2015. "Application of a Central Composite Design for the Study of NO x Emission Performance of a Low NO x Burner," Energies, MDPI, vol. 8(5), pages 1-22, April.
    19. SYAFITRI, Utami & SARTONO, Bagus & GOOS, Peter, 2015. "D- and I-optimal design of mixture experiments in the presence of ingredient availability constraints," Working Papers 2015003, University of Antwerp, Faculty of Business and Economics.
    20. Steven G. Gilmour & Peter Goos, 2009. "Analysis of data from non‐orthogonal multistratum designs in industrial experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 467-484, September.

    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:ant:wpaper:2013027. 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: Joeri Nys (email available below). General contact details of provider: https://edirc.repec.org/data/ftufsbe.html .

    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.