Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets
Jin Chu Wu, Alvin F. Martín, Raghu N. Kacker · Communications in Statistics - Simulation and Computation · 2015
The nonparametric two-sample bootstrap is applied to computing uncertainties of measures in ROC analysis on large datasets in areas such as biometrics, speaker recognition, etc., when the analytical method cannot be used. Its validation was studied by computing the SE of the area under ROC curve using the well-established analytical Mann-Whitney-statistic method and also using the bootstrap. The analytical result is unique. The bootstrap results are expressed as a probability distribution due to its stochastic nature. The comparisons were carried out using relative errors and hypothesis testing. They match very well. This validation provides a sound foundation for such computations.