This article introduces an informative goodness-of-fit (iGOF) approach to study multivariate distributions. When the null model is rejected, iGOF allows us to identify the underlying sources of mismodeling and naturally equips practitioners with additional insights on the nature of the deviations from the true distribution. The informative character of the procedure is achieved by exploiting smooth tests and random fields theory to facilitate the analysis of multivariate data. Simulation studies show that iGOF enjoys high power for different types of alternatives. The methods presented here directly address the problem of background mismodeling arising in physics and astronomy. It is in these areas that the motivation of this work is rooted.

翻译：本条提出了研究多变量分布的明智的实用(iGOF)方法。当无效模型被否决时,iGOF允许我们找出错误模型的深层来源,自然地使从业者更深入了解偏离真实分布的性质。该程序的信息性质是通过利用顺利测试和随机场理论来帮助分析多变量数据来实现的。模拟研究表明,iGOF对不同类型的替代方法拥有很高的力量。这里介绍的方法直接解决了物理和天文学中出现的背景模型错误问题。正是在这些领域,这项工作的动力是扎根的。