IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v89y2002i2p401-409.html
   My bibliography  Save this article

A type of restricted maximum likelihood estimator of variance components in generalised linear mixed models

Author

Listed:
  • J. G. Liao

Abstract

The maximum likelihood estimator of the variance components in a linear model can be biased downwards. Restricted maximum likelihood (REML) corrects this problem by using the likelihood of a set of residual contrasts and is generally considered superior. However, this original restricted maximum likelihood definition does not directly extend beyond linear models. We propose a REML-type estimator for generalised linear mixed models by correcting the bias in the profile score function of the variance components. The proposed estimator has the same consistency properties as the maximum likelihood estimator if the number of parameters in the mean and variance components models remains fixed. However, the estimator of the variance components has a smaller finite sample bias. A simulation study with a logistic mixed model shows that the proposed estimator is effective in correcting the downward bias in the maximum likelihood estimator. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • J. G. Liao, 2002. "A type of restricted maximum likelihood estimator of variance components in generalised linear mixed models," Biometrika, Biometrika Trust, vol. 89(2), pages 401-409, June.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:401-409
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Meza, Cristian & Jaffrézic, Florence & Foulley, Jean-Louis, 2009. "Estimation in the probit normal model for binary outcomes using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1350-1360, February.
    2. Salomon J. Sand & Dietrich Von Rosen & Agneta Falk Filipsson, 2003. "Benchmark Calculations in Risk Assessment Using Continuous Dose‐Response Information: The Influence of Variance and the Determination of a Cut‐Off Value," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1059-1068, October.
    3. Stefan Hirsch & Jan Schiefer, 2016. "What Causes Firm Profitability Variation in the EU Food Industry? A Redux of Classical Approaches of Variance Decomposition," Agribusiness, John Wiley & Sons, Ltd., vol. 32(1), pages 79-92, January.

    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:oup:biomet:v:89:y:2002:i:2:p:401-409. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.