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最小二乘预测与常规概率抽样方案的相对效率研究AI 解读

A particular finite population, typical of the type encountered in practice, is used to assess the efficiency of ratio estimation of the finite population total using conventional unrestricted random sampling, extreme and balanced sampling plans, for samples of size 10, 50, 100, and 200. The balanced sampling plans are seen to be as much as 30 percent more efficient than the corresponding unrestricted random sampling plan regardless of sample size, but the extreme sample is…

最小二乘预测相对于常规概率抽样方案的效率研究AI 解读

A particular finite population, typical of the type encountered in practice, is used to assess the efficiency of ratio estimation of the finite population total using conventional unrestricted random sampling, extreme and balanced sampling plans, for samples of size 10, 50, 100, and 200. The balanced sampling plans are seen to be as much as 30 percent more efficient than the corresponding unrestricted random sampling plan regardless of sample size, but the extreme sample is…

有限总体中的稳健估计（二）：按规模变量分层AI 解读

It has been established in Part I [3] that for estimating a finite population total, the estimators which are optimal under simple super-population models can be made insensitive to certain departures from the models by the choice of balanced samples. Here stratification on a size variable is considered as another technique for protecting against model failure. Together the two techniques, stratification and balanced sampling, are shown to provide more efficient protection…

有限总体中的稳健估计（一）AI 解读

This is an application of a least-squares prediction approach to finite population sampling theory. One way in which this approach differs from the conventional one is its focus on characteristics of particular samples rather than on plans for choosing samples. Here we study samples in which many superpopulation models lead to the same optimal (BLU) estimator. Random sampling is considered in the light of these results.

It has been established in Part I [3] that for estimating a finite population total, the estimators which are optimal under simple super-population models can be made insensitive to certain departures from the models by the choice of balanced samples. Here stratification on a size variable is considered as another technique for protecting against model failure. Together the two techniques, stratification and balanced sampling, are shown to provide more efficient protection…