An Optimal Test for Variance Components of Multivariate Mixed-Effects Linear Models.
An Optimal Test for Variance Components of Multivariate Mixed-Effects Linear Models.
复制标题
多元混合效应线性模型方差分量的最优检验。
DOI：
10.1016/j.jmva.2013.10.014
复制
发表时间：
2014
影响因子：
1.6
通讯作者：
Gibbons,RobertD
中科院分区：
文献类型：
--
作者：
Aryal,Subhash;Bhaumik,DulalK;Mathew,Thomas;Gibbons,RobertD
在本文中，我们推导了一个最佳检验，用于测试两个多元混合效应线性模型的随机效应协方差矩阵的显着性。我们通过模拟为不平衡设计设置的双变量中的各种替代假设来计算这个新导出的检验的功效，并观察到当样本大小和替代假设发生变化时，功效会急剧响应。对于一些平衡设计，我们通过模拟将最优检验的功效与似然比检验的功效进行比较，发现所提出的检验比似然比检验具有更大的功效。结果是使用人类生长的真实数据来说明的。重点介绍了该模型的其他相关应用。
In this article we derive an optimal test for testing the significance of covariance matrices of random-effects of two multivariate mixed-effects linear models. We compute the power of this newly derived test via simulation for various alternative hypotheses in a bivariate set up for unbalanced designs and observe that power responds sharply when sample size and alternative hypotheses are changed. For some balanced designs we compare power of the optimal test to that of the likelihood ratio test via simulation, and find that the proposed test has greater power than the likelihood ratio test. The results are illustrated using real data on human growth. Other relevant applications of the model are highlighted.