通过平滑推理和图形的穷举拟合优度
Exhaustive goodness-of-fit via smoothed inference and graphics
摘要：
经典的拟合优度检验旨在验证假设模型与所研究数据的一致性。鉴于它们的推理性质，它们可以被认为是验证性数据分析的关键步骤。然而，在他们的标准公式中，他们不允许探索假设模型如何偏离事实，也不提供任何关于如何改进被拒绝的模型以更好地拟合数据的见解。这项工作的主要目标是建立一个全面的框架，自然集成建模，估计，推理和图形的拟合优度。建模和估计的重点是一种新的配方，光滑的测试，很容易扩展到任意分布，连续或离散。通过专门设计的平滑引导进行推断和适当的选择后调整，并通过称为CD图的详尽图形工具总结结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
