论文标题
一组新的工具，用于拟合优点验证
A new set of tools for goodness-of-fit validation
论文作者
论文摘要
我们介绍了两个新工具来评估统计分布的有效性。这些工具基于从新的统计数量（$比较$ $ curve $）得出的组件。第一个工具是这些组件在$ bar $ $ $ plot $（b图）上的图形表示，可以提供对统计模型有效性的详细评估，特别是当由与模型相关的接受区域补充时。从该表示形式中获得的知识有时可能建议现有的$ goodnes $ - $ - $ - $ fit $ test，以控制I型错误的视觉评估。否则，自适应测试可能是可取的，第二个工具是这些组件的组合，以产生强大的$χ^2 $ -Type拟合优度测试。由于这些组件的数量可能很大，因此我们引入了一项新的选择规则，以数据驱动的方式决定其适当的数字以考虑。在模拟中，我们的合适性测试被认为是在完全指定模型以及必须估算某些参数时推荐的最佳解决方案的权力竞争。实际示例说明了如何使用这些工具来得出有关模型与数据偏离的原则信息。
We introduce two new tools to assess the validity of statistical distributions. These tools are based on components derived from a new statistical quantity, the $comparison$ $curve$. The first tool is a graphical representation of these components on a $bar$ $plot$ (B plot), which can provide a detailed appraisal of the validity of the statistical model, in particular when supplemented by acceptance regions related to the model. The knowledge gained from this representation can sometimes suggest an existing $goodness$-$of$-$fit$ test to supplement this visual assessment with a control of the type I error. Otherwise, an adaptive test may be preferable and the second tool is the combination of these components to produce a powerful $χ^2$-type goodness-of-fit test. Because the number of these components can be large, we introduce a new selection rule to decide, in a data driven fashion, on their proper number to take into consideration. In a simulation, our goodness-of-fit tests are seen to be powerwise competitive with the best solutions that have been recommended in the context of a fully specified model as well as when some parameters must be estimated. Practical examples show how to use these tools to derive principled information about where the model departs from the data.
