A recent methodology for extracting archetypal profiles from three-way asymmetric proximity data is applied to a dataset comprising 23 x 23 x 3 two-mode, three-way asymmetric dissimilarity matrices. The asymmetric dissimilarities are based on the oriented wavelet squared coherence of 10-year government bond yields among 23 European countries across three time intervals from 2001 to the present. First, the h-plot is computed for the unconditional two-mode three-way data, which is then represented in a unified Euclidean space, providing an intuitive and interpretable visualization. Subsequently, archetypoid analysis is performed. This unsupervised methodology identifies the archetypal countries and expresses all remaining countries as mixtures of these archetypal instances. Additionally, the degree of asymmetry for each country is calculated, offering further insight into the structure of the dissimilarities. The dataset and code are provided to support reproducible research.

翻译：一种从三向非对称邻近数据中提取原型特征的新方法被应用于包含23×23×3双模三向非对称相异度矩阵的数据集。这些非对称相异度基于2001年至今三个时间段内23个欧洲国家十年期国债收益率的定向小波平方相干性。首先，对无条件双模三向数据计算h-plot，并将其表示在统一的欧氏空间中，从而提供直观且可解释的可视化结果。随后进行原型点分析。这种无监督方法识别出具有原型特征的国家，并将所有其他国家表示为这些原型实例的混合体。此外，计算每个国家的非对称程度，以进一步揭示相异度的结构特征。数据集和代码已提供以支持可重复研究。