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Semiautomatic robust regression clustering of international trade data
Statistical Methods & Applications ( IF 1 ) Pub Date : 2021-06-11 , DOI: 10.1007/s10260-021-00569-3
Francesca Torti 1 , Marco Riani 2 , Gianluca Morelli 2
Affiliation  

The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature.



中文翻译:

国际贸易数据的半自动稳健回归聚类

本文的目的是展示回归聚类中如何选择最相关的解决方案,分析其稳定性,并提供有关最佳组数的最佳组合、组间误差方差的限制因素和修剪水平的信息。该过程基于两个步骤。首先,我们将约束稳健多元聚类的信息标准推广到聚类加权模型的情况。与基于选择最小化信息准则(即BIC)的最佳解决方案的传统方法不同,我们将注意力集中在所谓的最佳稳定解决方案上。第二步,使用监控方法,我们选择微调因子的最佳值。最后,我们使用确认性前向搜索方法验证解决方案。一个基于有关欧盟口罩贸易的新数据集的激励示例显示了当前现有程序的局限性。建议的方法最初应用于稳健回归聚类文献中的一组众所周知的数据集。然后,我们将注意力集中在一组国际贸易数据集上,并提供了一种新颖的信息方式来更新随机启动方法中的子集。补充材料本着特刊的精神,深化了对贸易数据的分析,并将建议的方法与文献中现有的方法进行了比较。建议的方法最初应用于稳健回归聚类文献中的一组众所周知的数据集。然后,我们将注意力集中在一组国际贸易数据集上,并提供了一种新颖的信息方式来更新随机启动方法中的子集。补充材料本着特刊的精神,深化了对贸易数据的分析,并将建议的方法与文献中现有的方法进行了比较。建议的方法最初应用于稳健回归聚类文献中的一组众所周知的数据集。然后,我们将注意力集中在一组国际贸易数据集上,并提供了一种新颖的信息方式来更新随机启动方法中的子集。补充材料本着特刊的精神,深化了对贸易数据的分析,并将建议的方法与文献中现有的方法进行了比较。

更新日期:2021-06-11
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