Improved testing inference in mixed linear models
Improved testing inference in mixed linear models
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DOI：
10.1016/j.csda.2008.12.007
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发表时间：
2009-05-15
影响因子：
1.8
通讯作者：
Cribari-Neto, Francisco
中科院分区：
文献类型：
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作者：
Melo, Tatiane F. N.;Ferrari, Silvia L. P.;Cribari-Neto, Francisco
混合线性模型是重复测量研究中常用的模型。它们解释了从同一实验单位获得的观测结果之间的相关性。通常，观察的数量很少，因此重要的是使用结合了小样本修正的推理策略。在本文中，我们发展了混合线性模型固定效应推断的似然比检验的修正版本。特别地，我们导出了对这种检验的Bartlett修正，以及对由修正的轮廓似然函数得到的检验的Bartlett修正。我们的结果推广了[Zucker，D.M.，Lieberman，O.，Manor，O.，2000]中的结果。混合线性模型中改进的小样本推断：Bartlett修正和调整似然。皇家统计学会期刊B，62,827-838]通过允许对感兴趣的参数进行向量值。此外，我们的Bartlett校正允许随机效应的非线性协方差矩阵结构。我们报告了模拟结果，结果表明，与标准似然比检验相比，所提出的检验具有更好的有限样本行为。文中还给出了应用实例并进行了讨论。(C)2008爱思唯尔B.V.保留所有权利。
Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.