论文标题
关于任意多元模型的合适性测试
On goodness-of-fit tests for arbitrary multivariate models
论文作者
论文摘要
拟合优点测试通常用于数据分析中，以测试分布与一组数据的一致性。这些测试可用于检测在已知背景的未知信号，或在被知识较低的背景污染的实验中对所提出的信号分布设置限制。可以针对任何拟议分布的现成的非参数测试仅在单变量情况下可用。在本文中，我们将讨论如何为任意多元分布或多元数据生成模型构建合适性测试。
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.
