论文标题
通过平滑的推理和图形详尽的融合优点
Exhaustive goodness-of-fit via smoothed inference and graphics
论文作者
论文摘要
合适性的经典测试旨在验证假定模型与所研究数据的一致性。鉴于它们的推论性，可以将其视为确认数据分析的关键步骤。但是，在其标准配方中，他们不允许探索假设的模型如何偏离真理，也不能提供有关如何改进被拒绝模型以更好地拟合数据的任何见解。这项工作的主要目的是建立一个综合框架，以自然整合建模，估计，推理和图形。建模和估计集中在平滑测试的新表述上，该测试很容易扩展到连续或离散的任意分布。推理和适当的截面调整是通过专门设计的平滑引导程序进行的，结果通过称为CD-plot的详尽图形工具来汇总结果。
Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. Given their inferential nature, they can be considered a crucial step in confirmatory data analysis. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. The main goal of this work is to establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference, and graphics. Modeling and estimation focus on a novel formulation of smooth tests that easily extends to arbitrary distributions, either continuous or discrete. Inference and adequate post-selection adjustments are performed via a specially designed smoothed bootstrap and the results are summarized via an exhaustive graphical tool called CD-plot.
