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Article Dans Une Revue Pattern Recognition Letters Année : 2014

Building confidence regions for the ROC surface

Résumé

The ROC surface is the major criterion for assessing the accuracy of diagnosis test statistics s (X) in regard to their capacity of discriminating between K ≥ 3 statistical populations. It provides additionally a widely used visual tool in the cases K = 2 and K = 3; It is the main purpose of this paper to investigate how to bootstrap a natural empirical estimator of the ROC surface in order to build accurate confidence regions in the ROC space. We first introduce a resampling procedure based on smooth versions of the empirical distributions involved to construct non Gaussian confidence regions. Simulation results are then displayed to show that such a “smoothed bootstrap” technique is preferable to a “naive” bootstrap approach in this situation. The accuracy of the method proposed is also illustrated using a psychometric dataset. An asymptotic analysis providing a rigorous theoretical basis for the method proposed is finally carried out in a functional framework.
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Dates et versions

hal-02107404 , version 1 (23-04-2019)

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Stéphan Clémençon, Sylvain Robbiano. Building confidence regions for the ROC surface. Pattern Recognition Letters, 2014, 46, pp.67-74. ⟨10.1016/j.patrec.2014.04.021⟩. ⟨hal-02107404⟩
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