On bootstrapping the ROC curve

Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the "smoothed bootstrap" is introduced. Theoretical arguments and simulation results are presented to show that the "smoothed bootstrap" is preferable to a "naive" bootstrap in order to construct accurate confidence bands.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
PublisherNeural Information Processing Systems
Pages137-144
Number of pages8
ISBN (Print)9781605609492
Publication statusPublished - 1 Jan 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: 8 Dec 200811 Dec 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Conference

Conference22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Country/TerritoryCanada
CityVancouver, BC
Period8/12/0811/12/08

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