
This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the "smoothed bootstrap" is introduced. Theoretical arguments and simulation results are presented to show that the "smoothed bootstrap" is preferable to a "naive" bootstrap in order to construct accurate confidence bands.
[MATH.MATH-PR] Mathematics [math]/Probability [math.PR], [MATH] Mathematics [math], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
[MATH.MATH-PR] Mathematics [math]/Probability [math.PR], [MATH] Mathematics [math], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
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