论文标题
多元分布的内容丰富的拟合优度
Informative Goodness-of-Fit for Multivariate Distributions
论文作者
论文摘要
本文介绍了研究多元分布的信息拟合优度（IGOF）方法。当无效模型被拒绝时，IGOF允许我们识别不隔底层的基本来源，并且自然地使从业者对与真实分布的偏差性质有了更多的见解。通过利用平滑测试和随机场理论来促进多元数据的分析，可以实现该过程的信息。仿真研究表明，IGOF对于不同类型的替代方案具有高功率。此处介绍的方法直接解决了物理和天文学中产生的背景不隔底层的问题。正是在这些领域，这项工作的动机是植根的。
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.
