, 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 December 7, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets
Published
Author(s)
, ,
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 MannWhitney 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
Keywords
Biometrics, Bootstrap, Large datasets, ROC analysis, Speaker recognition, Uncertainty, Validation
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created May 26, 2016, Updated November 10, 2018