A simple test for random effects in regression models

Testing that random effects are zero is difficult, because the null hypothesis restricts the corresponding variance parameter to the edge of the feasible parameter space. In the context of generalized linear mixed models, this paper exploits the link between random effects and penalized regression t...

Full description

Saved in:
Bibliographic Details
Published in:Biometrika 2013-12, Vol.100 (4), p.1005-1010
Main Author: Wood, Simon N.
Format: Article
Language:English
Subjects:
Generalized linear models
Hypotheses
Hypothesis testing
Miscellanea
Probability distribution
Random variables
Regression analysis
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Testing that random effects are zero is difficult, because the null hypothesis restricts the corresponding variance parameter to the edge of the feasible parameter space. In the context of generalized linear mixed models, this paper exploits the link between random effects and penalized regression to develop a simple test for a zero effect. The idea is to treat the variance components not being tested as fixed at their estimates and then to express the likelihood ratio as a readily computed quadratic form in the predicted values of the random effects. Under the null hypothesis this has the distribution of a weighted sum of squares of independent standard normal random variables. The test can be used with generalized linear mixed models, including those estimated by penalized quasilikelihood.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/ast038