Statistics – Applications
Scientific paper
2008-02-08
Statistics Surveys 2008, Vol. 2, 154-169
Statistics
Applications
Published in at http://dx.doi.org/10.1214/08-SS036 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of
Scientific paper
10.1214/08-SS036
An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly related to the fixed covariate effects and random effects. Therefore, it is of practical importance to test the adequacy of this assumption, particularly the assumption of linear covariate effects. In this paper, we review procedures that can be used for testing polynomial covariate effects in these popular models. Specifically, four types of hypothesis testing approaches are reviewed, i.e. R tests, likelihood ratio tests, score tests and residual-based tests. Derivation and performance of each testing procedure will be discussed, including a small simulation study for comparing the likelihood ratio tests with the score tests.
Huang Mingyan
Zhang Daowen
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