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Bootstrap Variability Studies in ROC Analysis on Large Datasets

Published

Author(s)

Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker

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

Citation

, 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 April 16, 2024)
Created March 19, 2014, Updated February 19, 2017