利用计算机视觉模型自动评估残差图
Automated Assessment of Residual Plots with Computer Vision Models
AI总结：
本研究针对lineup protocol残差图检验依赖人工、扩展性有限的问题，提出基于Kullback-Leibler散度训练的计算机视觉模型自动评估残差图，其敏感度低于传统检验但高于人工检验，可辅助线性模型诊断。
摘要：
绘制残差是诊断线性模型假设偏差（如非线性、异方差性和非正态性）的推荐流程。残差图中结构的存在性可通过lineup protocol（列队协议）进行视觉推断检验。现有多种传统残差检验方法，但用作统计检验的lineup protocol在诊断目的下表现更优，因为它敏感度更低，且能更广泛地应用于不同类型的偏差情况。然而，lineup protocol依赖人工判断，这限制了其可扩展性。本研究提出了一种解决方案：提供一个计算机视觉模型来实现残差图评估的自动化。该模型被训练用于预测一个距离度量，该度量基于Kullback-Leibler散度，量化拟合的经典正态线性回归模型的残差分布与参考分布之间的差异。通过大量模拟研究发现，该计算机视觉模型的敏感度低于传统检验，但高于人类视觉检验。它在非线性模式上的效果稍差。来自经典论文和当代数据的若干示例阐释了这一新流程，凸显了其在自动化诊断流程和补充现有方法方面的实用性。
英文摘要：
Plotting the residuals is a recommended procedure to diagnose deviations from linear model assumptions, such as non-linearity, heteroscedasticity, and non-normality. The presence of structure in residual plots can be tested using the lineup protocol to do visual inference. There are a variety of conventional residual tests, but the lineup protocol, used as a statistical test, performs better for diagnostic purposes because it is less sensitive and applies more broadly to different types of departures. However, the lineup protocol relies on human judgment which limits its scalability. This work presents a solution by providing a computer vision model to automate the assessment of residual plots. It is trained to predict a distance measure that quantifies the disparity between the residual distribution of a fitted classical normal linear regression model and the reference distribution, based on Kullback-Leibler divergence. From extensive simulation studies, the computer vision model exhibits lower sensitivity than conventional tests but higher sensitivity than human visual tests. It is slightly less effective on non-linearity patterns. Several examples from classical papers and contemporary data illustrate the new procedures, highlighting its usefulness in automating the diagnostic process and supplementing existing methods.
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