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摘要暂缺

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…

回归估计量的可容许性AI 解读

SUMMARY Admissibility of the regression estimator is established for two types of sampling designs: single stage sampling using probability proportional to a size variable and double sampling.

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…

摘要暂缺

二维抽样的若干最优设计AI 解读

Employing a superpopulation model, we derive conditions for which a sampling design of a two–dimensional finite population is optimal in the sense of minimum average variance, when the estimator of the population mean is the sample mean. We find that an overall optimal design does not exist, but that, if we consider three subclasses of two–dimensional sampling designs, then the optimal design within each subclass is a type of systematic sampling.

二维抽样的若干最优设计AI 解读

Employing a superpopulation model, we derive conditions for which a sampling design of a two–dimensional finite population is optimal in the sense of minimum average variance, when the estimator of the population mean is the sample mean. We find that an overall optimal design does not exist, but that, if we consider three subclasses of two–dimensional sampling designs, then the optimal design within each subclass is a type of systematic sampling.