Bootstrap Variability Studies in ROC Analysis on Large Datasets

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, e...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2014-01, Vol.43 (1), p.225-236
Main Authors: Wu, Jin Chu, Martin, Alvin F., Kacker, Raghu N.
Format: Article
Language:English
Subjects:
Algorithms
Biometrics
Bootstrap replications
Bootstrap variability
Computation
Computer simulation
Criminology
Large datasets
Panic attacks
Receivers
Replication
ROC analysis
Self help
Stalking
Tolerances
Uncertainty
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Summary: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.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2012.700362