Jump to ContentJump to Main Navigation
Show Summary Details
More options …

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year

IMPACT FACTOR 2017: 0.840
5-year IMPACT FACTOR: 1.000

CiteScore 2017: 0.97

SCImago Journal Rank (SJR) 2017: 1.150
Source Normalized Impact per Paper (SNIP) 2017: 1.022

Mathematical Citation Quotient (MCQ) 2016: 0.09

See all formats and pricing
More options …

A Smooth ROC Curve Estimator Based on Log-Concave Density Estimates

Kaspar Rufibach
Published Online: 2012-04-20 | DOI: https://doi.org/10.1515/1557-4679.1378

We introduce a new smooth estimator of the ROC curve based on log-concave density estimates of the constituent distributions. We show that our estimate is asymptotically equivalent to the empirical ROC curve if the underlying densities are in fact log-concave. In addition, we empirically show that our proposed estimator exhibits an efficiency gain for finite sample sizes with respect to the standard empirical estimate in various scenarios and that it is only slightly less efficient, if at all, compared to the fully parametric binormal estimate in case the underlying distributions are normal. The estimator is also quite robust against modest deviations from the log-concavity assumption. We show that bootstrap confidence intervals for the value of the ROC curve at a fixed false positive fraction based on the new estimate are on average shorter compared to the approach by Zhou and Qin (2005), while maintaining coverage probability. Computation of our proposed estimate uses the R package logcondens that implements univariate log-concave density estimation and can be done very efficiently using only one line of code. These obtained results lead us to advocate our estimate for a wide range of scenarios.

Keywords: diagnostic test; log-concave density estimation; nonparametric estimation; receiver operating characteristic curve; sensitivity and specificity

About the article

Published Online: 2012-04-20

Citation Information: The International Journal of Biostatistics, Volume 8, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1378.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Pablo Martínez-Camblor and Juan Carlos Pardo-Fernández
Statistical Methods in Medical Research, 2017, Page 096228021774078
Leandro García Barrado, Els Coart, and Tomasz Burzykowski
Statistics in Medicine, 2016, Volume 35, Number 4, Page 595

Comments (0)

Please log in or register to comment.
Log in