论文标题
具有任意大小的多元种群的非参数聚类
Non-parametric Clustering of Multivariate Populations with Arbitrary Sizes
论文作者
论文摘要
我们提出了一个聚类过程，将K组群体组为具有相同依赖性结构的亚组。该方法适用于配对种群，可以与面板数据一起使用。它依赖于从K种群估计的K密度Copulas的正交投影系数之间的差异。然后，每个群集由具有显着相似依赖性结构的种群组成。 Ngounou-Bakam和Pommeret（2022）的最新测试统计量用于自动构建此类簇。该过程是数据驱动的，取决于测试的渐近水平。我们通过数值研究和两个真实数据集说明了聚类算法：一组财务数据集和损失的保险数据集和分配的损失调整费用。
We propose a clustering procedure to group K populations into subgroups with the same dependence structure. The method is adapted to paired population and can be used with panel data. It relies on the differences between orthogonal projection coefficients of the K density copulas estimated from the K populations. Each cluster is then constituted by populations having significantly similar dependence structures. A recent test statistic from Ngounou-Bakam and Pommeret (2022) is used to construct automatically such clusters. The procedure is data driven and depends on the asymptotic level of the test. We illustrate our clustering algorithm via numerical studies and through two real datasets: a panel of financial datasets and insurance dataset of losses and allocated loss adjustment expense.
