In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; However, we often have access to easily attainable characteristics about sampling units. These characteristics are not typically employed in the data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. In this paper, we develop methods based on JPS samples for the estimation of categorical ordinal populations. We develop various estimators from JPS data even for a situation where JPS suffers from empty strata. We also propose JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in the estimation of the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of women aged 50 and older.

翻译：在需要成本效益的调查中，例如医学研究，测量感兴趣的变量（如疾病状态）通常成本高昂且/或耗时；然而，我们常常能够获取关于抽样单元中易于获得的特征。这些特征在数据收集过程中通常未被利用。判断事后分层（JPS）抽样使我们能够利用这些特征作为排序信息，对来自目标总体的随机样本进行补充。在本文中，我们基于JPS样本开发了用于估计分类有序总体参数的方法。我们提出了多种基于JPS数据的估计量，甚至适用于JPS出现空层的情况。此外，我们还提出了利用多重排序资源的JPS估计量。通过大量的数值研究，我们评估了这些方法在总体参数估计中的表现。最后，所开发的估计方法被应用于骨矿物质数据，以评估50岁及以上女性的骨骼疾病状况。