, J.
, Martin, A.
and Kacker, R.
(2014),
Bootstrap Variability Studies in ROC Analysis on Large Datasets, Communications in Statistics Part B-Simulation and Computation, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915595
(Accessed October 16, 2025)
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Bootstrap Variability Studies in ROC Analysis on Large Datasets
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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
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Journals
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Keywords
Bootstrap variability, Bootstrap replications, ROC analysis, Large datasets, Uncertainty, Biometrics
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Created March 19, 2014, Updated February 19, 2017