IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v87y2024i7d10.1007_s00184-023-00937-x.html
   My bibliography  Save this article

Bayesian minimum aberration mixed-level split-plot designs

Author

Listed:
  • Hui Li

    (Nankai University
    Chinese Academy of Sciences)

  • Min-Qian Liu

    (Nankai University)

  • Jinyu Yang

    (Nankai University)

Abstract

Many industrial experiments involve factors with levels more difficult to change or control than others, which leads to the development of two-level fractional factorial split-plot (FFSP) designs. Recently, mixed-level FFSP designs were proposed due to the requirement of different-level factors. In this paper, we generalize the Bayesian optimal criterion for mixed two- and four-level FFSP designs, and then provide Bayesian minimum aberration (MA) criterion to rank FFSP designs. Bayesian MA criterion can give a natural ordering for the effects involving two-level factors and three components of a four-level factor. We also discuss the relationship between the Bayesian optimal and Bayesian MA criteria. Furthermore, we consider the designs with both qualitative and quantitative factors.

Suggested Citation

  • Hui Li & Min-Qian Liu & Jinyu Yang, 2024. "Bayesian minimum aberration mixed-level split-plot designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(7), pages 889-906, October.
    • Handle: RePEc:spr:metrik:v:87:y:2024:i:7:d:10.1007_s00184-023-00937-x
    DOI: 10.1007/s00184-023-00937-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00184-023-00937-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00184-023-00937-x?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. V. Roshan Joseph & Mingyao AI & C. F. Jeff Wu, 2009. "Bayesian-inspired minimum aberration two- and four-level designs," Biometrika, Biometrika Trust, vol. 96(1), pages 95-106.
    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.

      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:metrik:v:87:y:2024:i:7:d:10.1007_s00184-023-00937-x. 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.