IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2022i24p8828-8843.html
   My bibliography  Save this article

D-optimal designs for mixture experiments with various correlation structures

Author

Listed:
  • Chang Li
  • Chongqi Zhang

Abstract

Mixture experimental designs have been in widespread use in agricultural, pharmaceutical, and other industrial research for many years. Much of the previous work mainly focuses on optimal design for mixture experiments when observations are uncorrelated, in large part because of the intractability of the optimal mixture experimental design on correlated case. When observations have certain correlation structures within each block, the order of the observations in each block matters and this order impacts the optimality of the design. Thus, there is need of research into construction of these useful designs when correlation structures might exist within blocks. In this paper, we propose D-optimal minimum support designs for Scheffe’s quadratic mixture model with odd number of components when observations in blocks are circulant correlated and hub correlated respectively, and Scheffe’s mixture model with any components when observations are in block-structured correlation.

Suggested Citation

  • Chang Li & Chongqi Zhang, 2022. "D-optimal designs for mixture experiments with various correlation structures," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(24), pages 8828-8843, May.
  • Handle: RePEc:taf:lstaxx:v:52:y:2022:i:24:p:8828-8843
    DOI: 10.1080/03610926.2022.2076117
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2022.2076117
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2022.2076117?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:lstaxx:v:52:y:2022:i:24:p:8828-8843. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.