标题： 基于类别-实数有序对推断分类数据顺序的非参数框架 显示英文标题

标题： A nonparametric framework for inferring orders of categorical data from category-real ordered pairs

摘要： 给定一个职业和收入的数据集，任意两个职业之间的收入差异有多大？ 给定一个旅行时间记录的数据集，选择公共交通方式$A$而不是$B$旅行需要多花多久？ 在本文中，我们提出了一种框架，能够使用估计统计框架推断类别的顺序以及每个类别之间实数差异的大小。 不仅报告类别顺序是否存在，我们的框架还报告顺序中每对连续类别的差异大小。 在大型数据集中，与现有框架相比，我们的框架具有良好的可扩展性。 所提出的框架已应用于两个现实世界的案例研究：1）根据泰国孔敬府350,000个家庭的信息按收入对职业进行排序，以及 2）根据2000年至2016年间纳斯达克股票市场1060家公司的收盘价对行业进行排序。 职业排序的结果显示不同职业之间的收入不平等。 股市结果展示了行业主导地位的动态变化，这种变化可能随时间而变化。 我们的方法可以应用于任何具有类别-实数有序对的研究领域。 我们提出的“主导-分布网络”提供了一种新方法，以获得分析类别顺序的新见解。 该框架的软件可通过R包 EDOIF 提供给研究人员或实践者。 显示英文摘要

摘要： Given a dataset of careers and incomes, how large a difference of income between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportation mode $A$ instead of $B$ to travel? In this paper, we propose a framework that is able to infer orders of categories as well as magnitudes of difference of real numbers between each pair of categories using Estimation statistics framework. Not only reporting whether an order of categories exists, but our framework also reports the magnitude of difference of each consecutive pairs of categories in the order. In large dataset, our framework is scalable well compared with the existing framework. The proposed framework has been applied to two real-world case studies: 1) ordering careers by incomes based on information of 350,000 households living in Khon Kaen province, Thailand, and 2) ordering sectors by closing prices based on 1060 companies' closing prices of NASDAQ stock markets between years 2000 and 2016. The results of careers ordering show income inequality among different careers. The stock market results illustrate dynamics of sector domination that can change over time. Our approach is able to be applied in any research area that has category-real ordered pairs. Our proposed "Dominant-Distribution Network" provides a novel approach to gain new insight of analyzing category orders. The software of this framework is available for researchers or practitioners within R package: EDOIF.