Goodness-of-fit tests are often used in data analysis to test the agreement of a distribution to a set of data. These tests can be used to detect an unknown signal against a known background or to set limits on a proposed signal distribution in experiments contaminated by poorly understood backgrounds. Out-of-the-box non-parametric tests that can target any proposed distribution are only available in the univariate case. In this paper, we discuss how to build goodness-of-fit tests for arbitrary multivariate distributions or multivariate data generation models.

翻译：关于任意多元模型的拟合优度检验 翻译摘要: 拟合优度检验在数据分析中通常用于检验一个分布是否与一组数据符合一致。这些测试可用于检测未知信号与已知背景的一致性，或者在受到理解不佳的背景干扰的实验中，对所提出的信号分布进行限制。目前仅限于单变量情况下，提供可针对任意提出的分布的非参数测试。在本文中，我们讨论了如何构建针对任意多元分布或多元数据生成模型的拟合优度检验。