IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v5y2021i2p88-94.html
   My bibliography  Save this article

D-optimal population designs in linear mixed effects models for multiple longitudinal data

Author

Listed:
  • Hongyan Jiang
  • Rongxian Yue

Abstract

The main purpose of this paper is to investigate D-optimal population designs in multi-response linear mixed models for longitudinal data. Observations of each response variable within subjects are assumed to have a first-order autoregressive structure, possibly with observation error. The equivalence theorems are provided to characterise the D-optimal population designs for the estimation of fixed effects in the model. The semi-Bayesian D-optimal design which is robust against the serial correlation coefficient is also considered. Simulation studies show that the correlation between multi-response variables has tiny effects on the optimal design, while the experimental costs are important factors in the optimal designs.

Suggested Citation

  • Hongyan Jiang & Rongxian Yue, 2021. "D-optimal population designs in linear mixed effects models for multiple longitudinal data," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 5(2), pages 88-94, April.
    • Handle: RePEc:taf:tstfxx:v:5:y:2021:i:2:p:88-94
    DOI: 10.1080/24754269.2021.1884444
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2021.1884444
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24754269.2021.1884444?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:tstfxx:v:5:y:2021:i:2:p:88-94. 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/tstf .

    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.