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Powerful nonparametric statistics to compare k independent ROC curves

Author

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  • Pablo Mart�nez-Camblor
  • Carlos Carleos
  • Norberto Corral

Abstract

The authors deal with the problem of comparing receiver operating characteristic (ROC) curves from independent samples. From a nonparametric approach, they propose and study three different statistics. Their asymptotic distributions are obtained and a resample plan is considered. In order to study the statistical power of the introduced statistics, a simulation study is carried out. The (observed) results suggest that, for the considered models, the new statistics are more powerful than the usually employed ones (the Venkatraman test and the usual area under the ROC curve criterion) in non-uniform dominance situations and quite good otherwise.

Suggested Citation

  • Pablo Mart�nez-Camblor & Carlos Carleos & Norberto Corral, 2011. "Powerful nonparametric statistics to compare k independent ROC curves," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1317-1332, May.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1317-1332
    DOI: 10.1080/02664763.2010.498504
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    References listed on IDEAS

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    1. Fair, Ray C, 1978. "A Theory of Extramarital Affairs," Journal of Political Economy, University of Chicago Press, vol. 86(1), pages 45-61, February.
    2. E. S. Venkatraman, 2000. "A Permutation Test to Compare Receiver Operating Characteristic Curves," Biometrics, The International Biometric Society, vol. 56(4), pages 1134-1138, December.
    3. Lopez-de-Ullibarri, Ignacio & Cao, Ricardo & Cadarso-Suarez, Carmen & Lado, Maria J., 2008. "Nonparametric estimation of conditional ROC curves: Application to discrimination tasks in computerized detection of early breast cancer," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2623-2631, January.
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    1. Meesun Sun & Kwanghyun Choi & Sungzoon Cho, 2015. "Estimating the minority class proportion with the ROC curve using Military Personality Inventory data of the ROK Armed Forces," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1677-1689, August.
    2. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Arís Fanjul-Hevia & Wenceslao González-Manteiga, 2018. "A comparative study of methods for testing the equality of two or more ROC curves," Computational Statistics, Springer, vol. 33(1), pages 357-377, March.
    4. Pablo Martínez-Camblor & Sonia Pérez-Fernández & Susana Díaz-Coto, 2021. "Optimal classification scores based on multivariate marker transformations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 581-599, December.

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