Variance component testing in generalised linear models with random effects
Variance component testing in generalised linear models with random effects
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DOI：
10.1093/biomet/84.2.309
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发表时间：
1997-06-01
期刊：
影响因子：
2.7
通讯作者：
Lin, XH
中科院分区：
文献类型：
--
作者：
Lin, XH
在生物医学研究中，有相当大的兴趣在测试的过度分散，相关性和异质性跨组。在本文中，我们投的框架中的广义线性模型的随机效应的问题。我们提出了一个全局得分检验的零假设，所有的方差分量为零，这个测试是一个局部渐近最严格的测试，是强大的特殊意义下，测试不需要指定的联合分布的随机效应。我们还提出了单独的分数测试和他们的近似分别测试方差分量。这两种测试都可以使用现有的统计软件轻松实现。我们说明了这些测试与应用程序的异质性的研究在蝾螈交配的实验中，男性和女性的交配成功，并通过模拟评估其性能。
There is considerable interest in testing for overdispersion, correlation and heterogeneity across groups in biomedical studies. In this paper, we cast the problem in the framework of generalised linear models with random effects. We propose a global score test for the null hypothesis that all the variance components are zero, This test is a locally asymptotically most stringent test and is robust in the special sense that the test does not require specifying the joint distribution of the random effects. We also propose individual score tests and their approximations for testing the variance components separately. Both tests can be easily implemented using existing statistical software. We illustrate these tests with an application to the study of heterogeneity of mating success across males and females in an experiment on salamander matings, and evaluate their performance through simulation.