Skip Navigation

Biometrika 2002 89(2):401-409; doi:10.1093/biomet/89.2.401
© 2002 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Liao, J. G.
Right arrow Articles by Lipsitz, S. R.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

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

J.G. Liao1 and Stuart R.Lipsitz2

1 Division of Biometrics, University of Medicine and Dentistry of New Jersey, 335 George Street, Suite 2200, New Brunswick, New Jersey 08903, U.S.A.jg_liao@yahoo.com 2 Department of Biometry and Epidemiology, Medical University of South Carolina, 135 Rutledge Avenue, Suite 1148, Charleston, South Carolina 29425, U.S.A. lipsitzs@musc.edu

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.

Key Words: Bias correction; EM algorithm; Logistic mixed model


Received July 2000. Revised September 2001


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.