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Publication Citation: Bootstrap Variability Studies in ROC Analysis on Large Datasets

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Author(s): Jin Chu Wu; Alvin Martin; Raghu N. Kacker;
Title: Bootstrap Variability Studies in ROC Analysis on Large Datasets
Published: March 19, 2014
Abstract: The nonparametric two-sample bootstrap is employed to compute uncertainties of measures in receiver operating characteristic (ROC) analysis on large datasets in areas such as biometrics, and so on. In this framework, the bootstrap variability was empirically studied without a normality assumption, exhaustively in five scenarios involving both high- and low-accuracy matching algorithms. With a tolerance 0.02 of the coefficient of variation, it was found that 2000 bootstrap replications were appropriate for ROC analysis on large datasets in order to reduce the bootstrap variance and ensure the accuracy of the computation.
Citation: Communications in Statistics Part B-Simulation and Computation
Keywords: Bootstrap variability; Bootstrap replications; ROC analysis; Large datasets; Uncertainty; Biometrics
Research Areas: Assessment, Statistics, Measurements
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