摘要翻译：
线性混合模型和广义线性混合模型的一个重要特征是，给定随机效应的响应的条件均值，经链接函数变换后，与固定协变效应和随机效应线性相关。因此，检验这一假设，尤其是线性协变效应假设的充分性具有重要的现实意义。在这篇文章中，我们回顾了在这些流行模型中检验多项式协变量效应的过程。具体而言，本文回顾了四种假设检验方法，即R检验、似然比检验、分数检验和残差检验。将讨论每个测试过程的推导和性能，包括一个小的模拟研究，以比较似然比测试和分数测试。
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英文标题：
《Testing polynomial covariate effects in linear and generalized linear
  mixed models》
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作者：
Mingyan Huang, Daowen Zhang
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最新提交年份：
2008
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/802.1103