Q-Q绘图的变化--我们眼睛的力量！
Variations of Q-Q Plots -- The Power of our Eyes!
摘要：
在统计建模中，我们努力指定类似于研究中收集的数据或从过程中观察到的数据的模型。因此，分布规格和参数估计是参数模型的核心。图形程序，如分位数-分位数（Q-Q）图，可以说是最广泛使用的分布评估方法，尽管批评者发现他们的解释过于主观。正式的拟合优度测试是可用的，并且非常强大，但仅指示是否存在拟合不足，而不是为什么存在拟合不足。在本文中，我们将探讨使用阵容协议注入严格的图形分布评估和比较其权力的正式分布测试。我们发现标准Q-Q图的排列比去趋势Q-Q图的排列更强大，并且排列检验比传统的正态性检验更强大。虽然我们专注于诊断非正态性，但我们的方法是通用的，可以直接扩展到其他分布的评估。
英文摘要：
In statistical modeling we strive to specify models that resemble data collected in studies or observed from processes. Consequently, distributional specification and parameter estimation are central to parametric models. Graphical procedures, such as the quantile-quantile (Q-Q) plot, are arguably the most widely used method of distributional assessment, though critics find their interpretation to be overly subjective. Formal goodness-of-fit tests are available and are quite powerful, but only indicate whether there is a lack of fit, not why there is lack of fit. In this paper we explore the use of the lineup protocol to inject rigor to graphical distributional assessment and compare its power to that of formal distributional tests. We find that lineups of standard Q-Q plots are more powerful than lineups of de-trended Q-Q plots and that lineup tests are more powerful than traditional tests of normality. While, we focus on diagnosing non-normality, our approach is general and can be directly extended to the assessment of other distributions.
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。
