Special Guest Lecture on Visual Inference with Claus Ekstrøm…

Join us for a special guest lecture (either in person or virtually) featuring Claus Ekstrøm, professor and vice-chair at the Section of Biostatistics, University of Copenhagen. His primary research interests are centered on developing methods for the analysis of high-dimensional data problems and causal discovery. He’s authored two books on statistics and is frequently used as an expert on statistics in Danish news media. Claus has been a grumpy old man from a young age.

Register Now  |  Wednesday 12/1/2021, 4:00-5:00 PM

This is a hybrid event:  In person at Otto G. Richter Library, CR 343  | or online via Zoom.

Title of the lecture:  “Validation of visual inference methods in statistics by use of deep learning”

When does inspecting a certain graphical plot allow for an investigator to reach the right statistical conclusion? Visual inference is commonly used for various tasks in statistics—including model diagnostics and exploratory data analysis – and though attractive due to its intuitive nature, the lack of available methods for validating plots is a major drawback.

We propose a new validation method for visual inference. Our method trains deep neural networks to distinguish between plots simulated under two different data-generating mechanisms (null or alternative), and we use the classification accuracy as a technical validation score (TVS). The TVS measures the information content in the plots, and TVS values can be used to compare different plots or different choices of data-generating mechanisms, thereby providing a meaningful scale that new visual inference procedures can be validated against.

We apply the method to three popular diagnostic plots for linear regression, namely the scatter plot, the quantile-quantile plot, and the residual plot. We consider various types and degrees of misspecification, as well as different within-plot sample sizes. Our method produces TVSs that increase with increasing sample size and decrease with increasing difficulty, and hence the TVS is a meaningful measure of validity.