Automated Assessment of Residual Plots with Computer Vision Models
This work addresses the scalability limitation of human judgment in residual plot diagnostics for statisticians and data analysts, representing an incremental improvement by supplementing existing methods.
The paper tackled the problem of automating the assessment of residual plots for diagnosing linear model assumptions by developing a computer vision model that predicts a distance measure based on Kullback-Leibler divergence, achieving lower sensitivity than conventional tests but higher sensitivity than human visual tests, with slightly reduced effectiveness on non-linearity patterns.
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.