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：拟合优度检验常被用于数据分析，以检验数据与某一分布的一致性。这类检验可用来在已知背景中探测未知信号，或是在受认知不足的背景污染的实验中，对拟议的信号分布设定上限。现成的非参数检验方法仅适用于单变量情况，且能针对任意提议分布。本文讨论如何构建适用于任意多元分布或多元数据生成模型的拟合优度检验。