Optimal ROC Curves from Score Variable Threshold Tests
Abstract
The Receiver Operating Characteristic (ROC) is a well-established representation of the tradeoff between detection and false alarm probabilities in binary hypothesis testing. In many practical contexts ROC's are generated by thresholding a measured score variable -- applying score variable threshold tests (SVT's). In many cases the resulting curve is different from the likelihood ratio test (LRT) ROC and is therefore not Neyman-Pearson optimal. While it is well-understood that concavity is a necessary condition for an ROC to be Neyman-Pearson optimal, this paper establishes that it is also a sufficient condition in the case where the ROC was generated using SVT's. It further defines a constructive procedure by which the LRT ROC can be generated from a non-concave SVT ROC, without requiring explicit knowledge of the conditional PDF's of the score variable. If the conditional PDF's are known, the procedure implicitly provides a way of redesigning the test so that it is equivalent to an LRT.
Cite
@article{arxiv.2012.08391,
title = {Optimal ROC Curves from Score Variable Threshold Tests},
author = {Catherine Medlock and Alan Oppenheim},
journal= {arXiv preprint arXiv:2012.08391},
year = {2020}
}