Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets

Published

May 26, 2016

Author(s)

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

Abstract

The nonparametric two-sample bootstrap is applied to computing uncertainties of measures in receiver operating characteristic (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 standard errors 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. These match very well. This validation provides a sound foundation for such computations.

Citation

Communications in Statistics Part B-Simulation and Computation

Issue

45

Pub Type

Journals

Download Paper

https://doi.org/10.1080/03610918.2015.1065327

Keywords

Biometrics, Bootstrap, Large datasets, ROC analysis, Speaker recognition, Uncertainty, Validation

Citation

, J. , Martin, A. and Kacker, R. (2016), Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets, Communications in Statistics Part B-Simulation and Computation, [online], https://doi.org/10.1080/03610918.2015.1065327 (Accessed October 27, 2025)