In survey sampling studies, statistical inference can be constructed either using design based randomisation or super population model. Design-based inference using judgement post-stratified (JPS) sampling is available in the literature. This paper develops statistical inference based on super population model in a finite population setting using JPS sampling design. For a JPS sample, first a simple random sample (SRS) is constructed without replacement. The sample units in this SRS are then stratified based on judgement ranking in a small comparison set to induce a data structure in the sample. The paper shows that the mean of a JPS sample is model unbiased and has smaller mean square prediction error (MSPE) than the MSPE of a simple random sample mean. Using an unbiased estimator of the MSPE, the paper also constructs prediction confidence interval for the population mean. A small-scale empirical study shows that the JPS sample predictor performs better than an SRS predictor when the quality of ranking information in JPS sampling is not poor. The paper also shows that the coverage probabilities of prediction intervals are very close to the nominal coverage probability. Proposed inferential procedure is applied to a real data set obtained from an agricultural research farm.

中文翻译：

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在有限总体中使用判断后分层样本的基于模型的推理

在调查抽样研究中，可以使用基于设计的随机化或超总体模型来构建统计推断。文献中提供了使用分层后判断 (JPS) 抽样的基于设计的推理。本文使用 JPS 抽样设计在有限总体设置中基于超总体模型开发统计推断。对于 JPS 样本，首先构造一个没有替换的简单随机样本 (SRS)。然后根据在一个小的比较集中的判断排名对这个 SRS 中的样本单元进行分层，以在样本中归纳出一个数据结构。该论文表明，JPS 样本的均值是模型无偏的，并且比简单随机样本均值的 MSPE 具有更小的均方预测误差 (MSPE)。使用 MSPE 的无偏估计量，该论文还构建了总体均值的预测置信区间。一项小规模的实证研究表明，当 JPS 抽样中的排名信息质量不差时，JPS 样本预测器的性能优于 SRS 预测器。该论文还表明，预测区间的覆盖概率非常接近名义覆盖概率。建议的推理程序应用于从农业研究农场获得的真实数据集。