IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v39y1990i3p341-356.html
   My bibliography  Save this article

Modelling in Repeated Measures Experiments

Author

Listed:
  • A. P. Verbyla
  • B. R. Cullis

Abstract

In repeated measures experiments how treatment contrasts change over time is often of prime interest and modelling these contrasts is the aim of any analysis. In long‐term experiments, the number of repeated measures can be large or may exceed the degrees of freedom for error. Modelling the covariance matrix is therefore either desirable or required. We present an approach to the analysis of repeated measures data in which both the mean and the covariance matrix are modelled parametrically. We use the correlogram and semivariogram for identification of the covariance structure and linear models for specifying the change over time of the treatment contrasts. Incomplete data are handled in the approach and estimation is based on residual maximum likelihood. Examples which motivated the work are presented to illustrate the applicability of the method.

Suggested Citation

  • A. P. Verbyla & B. R. Cullis, 1990. "Modelling in Repeated Measures Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(3), pages 341-356, November.
    • Handle: RePEc:bla:jorssc:v:39:y:1990:i:3:p:341-356
    DOI: 10.2307/2347384
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347384
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347384?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lindsey, P. J. & Lindsey, J. K., 2000. "Diagnostic tools for random effects in the repeated measures growth curve model," Computational Statistics & Data Analysis, Elsevier, vol. 33(1), pages 79-100, March.

    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:bla:jorssc:v:39:y:1990:i:3:p:341-356. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.