Model-free posterior inference on the area under the receiver operating characteristic curve
Methodology (stat.ME)
FOS: Computer and information sciences
0202 electrical engineering, electronic engineering, information engineering
02 engineering and technology
0101 mathematics
01 natural sciences
Statistics - Methodology
DOI:
10.1016/j.jspi.2020.03.008
Publication Date:
2020-04-10T00:35:30Z
AUTHORS (2)
ABSTRACT
The area under the receiver operating characteristic curve (AUC) serves as a summary of a binary classifier's performance. Methods for estimating the AUC have been developed under a binormality assumption which restricts the distribution of the score produced by the classifier. However, this assumption introduces an infinite-dimensional nuisance parameter and can be inappropriate, especially in the context of machine learning. This motivates us to adopt a model-free Gibbs posterior distribution for 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 methods based on a rank likelihood.<br/>19 pages, 3 figures, 5 tables<br/>
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