论文标题
新的基于图的多样本测试，用于高维和非欧盟数据
New graph-based multi-sample tests for high-dimensional and non-Euclidean data
论文作者
论文摘要
在许多字段中，测试多个样本的分布中的平等是一个常见的任务。但是，对于高维或非欧国人数据的这个问题尚未得到很好的探索。在本文中，我们根据基于来自多个样本的汇总观测值构建的相似性图提出了新的非参数测试，并使用样本内边缘和样本之间的边缘，这是一个直接但没有探索的想法。对于广泛的替代方案，新测试对现有测试的功率进行了重大改进。我们还研究了测试统计数据的渐近分布，为大型数据集提供了简单的现成工具。通过对年龄图像数据集的分析来说明新测试。
Testing the equality in distributions of multiple samples is a common task in many fields. However, this problem for high-dimensional or non-Euclidean data has not been well explored. In this paper, we propose new nonparametric tests based on a similarity graph constructed on the pooled observations from multiple samples, and make use of both within-sample edges and between-sample edges, a straightforward but yet not explored idea. The new tests exhibit substantial power improvements over existing tests for a wide range of alternatives. We also study the asymptotic distributions of the test statistics, offering easy off-the-shelf tools for large datasets. The new tests are illustrated through an analysis of the age image dataset.
