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合并独立检验的若干统计程序AI 解读

In many applications available data from several independent studies address the same question, and it is essential to have statistical methods for combining the results from the different studies. This article addresses this issue in two setups: (1) a testing hypothesis concerning the common mean vector of two independent linear models having different variances, and (2) a testing hypothesis concerning a common variance component in linear models involving two variance…

无交互作用的不平衡双向模型中的最优检验AI 解读

It is an open problem in the literature to derive optimum tests for the equality of treatment effects in an unbalanced two-way classification model. For such models without interaction, optimum tests are derived in the following cases: (i) the locally best invariant unbiased test for the random effects model corresponding to an equiblock and equireplicate design, (ii) the locally best invariant unbiased test for the mixed effects model with mixed treatment effects…

平衡模型中固定效应与方差分量的最优检验AI 解读

For any analysis-of-variance model with balanced data involving both fixed and random effects, uniformly most powerful unbiased (UMPU) and uniformly most powerful invariant unbiased (UMPIU) tests are derived for the significance of a fixed effect or a variance component, under the assumption of normality of random effects. The tests coincide with the usual F tests. Robustness of the UMPIU test against suitable deviations from normality is established.

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协方差矩阵完全可约时的均值向量并交检验AI 解读

The union-intersection test is derived for the mean vector of a multi-normal population when the covariance matrix is totally reducible. The performance of the test is compared to those of two other tests, the likelihood ratio test and a test discussed in Young (1976), using an information theory approach, by computing the small-sample powers through Monte Carlo techniques and also by computing the Pitman efficiency.