title: Validation of Nonparametric Two-Sample Bootstrap in ROC Analysis on Large Datasets.
abstract: 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.
authors: National Institute of Standards and Technology, Gaithersburg, MD 20899.
authors: Jin Chu
authors: JC
authors: Wu
authors: National Institute of Standards and Technology, Gaithersburg, MD 20899.
authors: Alvin F
authors: AF
authors: Martin
authors: National Institute of Standards and Technology, Gaithersburg, MD 20899.
authors: Raghu N
authors: RN
authors: Kacker
keywords: ROC analysis; biometrics; bootstrap; large datasets; speaker recognition; uncertainty; validation