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Activity Number: 171 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313201
Title: Model-Free Posterior Inference on the Area Under the Receiver Operating Characteristic Curve
Author(s): Zhe Wang* and Ryan Martin
Companies: North Carolina State University and North Carolina State University
Keywords: credible interval; Gibbs posterior; generalized Bayesian inference; model misspecification; robustness
Abstract:

The area under the receiver operating characteristic curve (AUC) serves as a summary of a binary classifier’s performance. For inference on the AUC, a common modeling assumption is binormality, which restricts the distribution of the score produced by the classifier. However, this assumption introduces an infinite-dimensional nuisance parameter and may be restrictive in certain machine learning settings. To avoid making distributional assumptions, and to avoid the computational challenges of a fully non-parametric analysis, we develop a direct and model-free Gibbs posterior distribution for inference on the AUC. We present the asymptotic Gibbs posterior concentration rate, and a strategy for tuning the learning rate so that the corresponding credible intervals achieve the nominal frequentist coverage probability. Simulation experiments and a real data analysis demonstrate the Gibbs posterior’s strong performance compared to existing Bayesian methods.


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