IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v58y1996i1p27-54.html
   My bibliography  Save this article

Covariate Screening in Mixed Linear Models

Author

Listed:
  • Richardson, A. M.
  • Welsh, A. H.

Abstract

We address the important practical problem of selecting covariates in mixed linear models when the covariance structure is known from the data collection process and there are a possibly large number of covariates available. In particular, we consider procedures which can be considered extensions of the analysis of deviance to mixed linear models. This approach provides an alternative to likelihood ratio test methodology which can be applied in the case that the components of variance are estimated by restricted maximum likelihood (REML), thus resolving the open question of how to proceed in this context. Moreover, it is simple to robustify and allows us to consider a wider class of procedures than those which fit into the simple likelihood ratio test framework. The key insights are that the deviance should be specified by the procedure used to estimate the fixed effects and that the estimated covariance matrix should be held fixed across different models for the fixed effects.

Suggested Citation

  • Richardson, A. M. & Welsh, A. H., 1996. "Covariate Screening in Mixed Linear Models," Journal of Multivariate Analysis, Elsevier, vol. 58(1), pages 27-54, July.
  • Handle: RePEc:eee:jmvana:v:58:y:1996:i:1:p:27-54
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(96)90038-X
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Dielman, Terry E. & Rose, Elizabeth L., 1996. "A note on hypothesis testing in LAV multiple regression: A small sample comparison," Computational Statistics & Data Analysis, Elsevier, vol. 21(4), pages 463-470, April.
    2. Dielman, Terry E. & Rose, Elizabeth L., 1995. "A bootstrap approach to hypothesis testing in least absolute value regression," Computational Statistics & Data Analysis, Elsevier, vol. 20(2), pages 119-130, August.
    Full references (including those not matched with items on IDEAS)

    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:jmvana:v:58:y:1996:i:1:p:27-54. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.