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2008 Testing polynomial covariate effects in linear and generalized linear mixed models
Mingyan Huang, Daowen Zhang
Statist. Surv. 2: 154-169 (2008). DOI: 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.


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Mingyan Huang. Daowen Zhang. "Testing polynomial covariate effects in linear and generalized linear mixed models." Statist. Surv. 2 154 - 169, 2008.


Published: 2008
First available in Project Euclid: 29 December 2008

zbMATH: 1189.62024
MathSciNet: MR2520984
Digital Object Identifier: 10.1214/08-SS036

Rights: Copyright © 2008 The author, under a Creative Commons Attribution License


Vol.2 • 2008
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