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协方差分析中的随机化AI 解读

When examining any linear contrast of adjusted treatment means in the analysis of covariance it is shown that balancing on the covariates is desirable in that it minimizes the model variance of the treatment contrast averaged over the randomization distribution. Approximate balancing, through truncation of the randomization distri bution, is examined empirically. It is shown empirically that robustness of the test statistic can be achieved through truncated randomization.…

存在自相关时实验的最优随机化AI 解读

For a linear or spatial ordering of the experimental units, systematic methods of applying the treatments to the experimental units minimize the variance of linear contrasts of treatment means. In the linear ordering the optimal randomization scheme for the application of k treatments is to apply a treatment to every kth experimental unit, the equivalent of systematic sampling in the sampling literature. In the spatial ordering the optimal randomization schemes are…

存在自相关时实验的最优随机化AI 解读

SUMMARY For a linear or spatial ordering of the experimental units, systematic methods of applying the treatments to the experimental units minimize the variance of linear contrasts of treatment means. In the linear ordering the optimal randomization scheme for the application of k treatments is to apply a treatment to every kth experimental unit, the equivalent of systematic sampling in the sampling literature. In the spatial ordering the optimal randomization schemes are…