IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v219y2025ics0167715224002803.html
   My bibliography  Save this article

Optimal split-plot designs under individual word length patterns

Author

Listed:
  • Han, Xiaoxue
  • Sheng, Chong
  • Liu, Min-Qian

Abstract

For multi-factor experiments that cannot run all the factors in a completely random order, fractional factorial split-plot (FFSP) designs are often used in practice. When some prior knowledge has shown that some factors are more likely to be significant than others, Han et al. (2023) proposed the individual word length patterns (IWLPs) of whole-plot (WP) and sub-plot (SP), denoted by the IwWLP and IsWLP respectively, in the FFSP design. In this paper, we propose a construction method for optimal FFSP designs based on these two criteria, where the key of the method is to construct generating matrices for different FFSP designs from the generating matrix of a fractional factorial design, and hence we get a class of effective FFSP designs. These designs are more applicable in many situations. The results for 16-run two-level FFSP designs are tabulated in the supplementary material for possible use by practitioners.

Suggested Citation

  • Han, Xiaoxue & Sheng, Chong & Liu, Min-Qian, 2025. "Optimal split-plot designs under individual word length patterns," Statistics & Probability Letters, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:stapro:v:219:y:2025:i:c:s0167715224002803
    DOI: 10.1016/j.spl.2024.110311
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715224002803
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2024.110311?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. Qianqian Zhao & Shengli Zhao, 2015. "Mixed-level designs with resolution III or IV containing clear two-factor interaction components," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 953-965, November.
    2. 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.
    3. Ai, Mingyao & Zhang, Runchu, 2004. "Multistratum fractional factorial split-plot designs with minimum aberration and maximum estimation capacity," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 161-170, August.
    4. Ching-Shui Cheng & Pi-Wen Tsai, 2009. "Optimal two-level regular fractional factorial block and split-plot designs," Biometrika, Biometrika Trust, vol. 96(1), pages 83-93.
    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. Zhang, Tianfang & Jiang, Junyu & Li, Zhiming, 2018. "Some families of asymmetric nested orthogonal arrays and asymmetric sliced orthogonal arrays," Statistics & Probability Letters, Elsevier, vol. 141(C), pages 109-113.
    2. T. I. Katsaounis, 2022. "On multistage experiments with restrictions in the randomization of treatments," Statistical Papers, Springer, vol. 63(2), pages 531-541, April.
    3. Li, Zhiming & Zhao, Shengli & Zhang, Runchu, 2015. "On general minimum lower order confounding criterion for s-level regular designs," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 202-209.
    4. 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.
    5. Wang, Xiaodi & Chen, Xueping & Zhang, Yingshan, 2018. "Feasible blocked multi-factor designs of unequal block sizes," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 102-109.
    6. Yan, Zhaohui & Zhao, Shengli, 2023. "Optimal fractions of three-level factorials under a baseline parameterization," Statistics & Probability Letters, Elsevier, vol. 202(C).

    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:stapro:v:219:y:2025:i:c:s0167715224002803. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.