This paper develops statistical inference for population mean and total using stratified judgment post-stratified (SJPS) samples. The SJPS design selects a judgment post-stratified sample from each stratum. Hence, in addition to stratum structure, it induces additional ranking structure within stratum samples. SJPS is constructed from a finite population using either a with or without replacement sampling design. Inference is constructed under both randomization theory and a super population model. In both approaches, the paper shows that the estimators of population mean and total are unbiased. The paper also constructs unbiased estimators for the variance (mean square prediction error) of the sample mean (predictor of population mean), and develops confidence and prediction intervals for the population mean. The empirical evidence shows that the proposed estimators perform better than their competitors in the literature.

中文翻译：

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使用有限群体的分层判断后分层样本进行统计推断

本文使用分层判断后分层（SJPS）样本，对总体平均值和总数进行统计推断。SJPS设计从每个层次中选择一个判断后分层的样本。因此，除了层次结构外，它还会在层次样本中引发其他排名结构。SJPS从使用有限的人口构造无论是用或不用更换抽样设计。推论是根据随机化理论和超总体模型构建的。在这两种方法中，本文都表明总体均值和总体的估计量是无偏的。本文还为样本均值（总体均值的预测变量）的方差（均方预测误差）构造了无偏估计量，并为总体均值建立了置信度和预测区间。经验证据表明，拟议的估计量在文献中比其竞争对手要好。