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A new method of kernel-smoothing estimation of the ROC curve

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  • Michał Pulit

    (Wrocław University of Technology)

Abstract

The receiver operating characteristic (ROC) curve is a popular graphical tool for describing the accuracy of a diagnostic test. Based on the idea of estimating the ROC curve as a distribution function, we propose a new kernel smoothing estimator of the ROC curve which is invariant under nondecreasing data transformations. We prove that the estimator has better asymptotic mean squared error properties than some other estimators involving kernel smoothing and we present an easy method of bandwidth selection. By simulation studies, we show that for the limited sample sizes, our proposed estimator is competitive with some other nonparametric estimators of the ROC curve. We also give an example of applying the estimator to a real data set.

Suggested Citation

  • Michał Pulit, 2016. "A new method of kernel-smoothing estimation of the ROC curve," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 603-634, July.
    • Handle: RePEc:spr:metrik:v:79:y:2016:i:5:d:10.1007_s00184-015-0569-1
    DOI: 10.1007/s00184-015-0569-1
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    References listed on IDEAS

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    1. Lloyd, Chris J. & Yong, Zhou, 1999. "Kernel estimators of the ROC curve are better than empirical," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 221-228, September.
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    Cited by:

    1. Ana M. Bianco & Graciela Boente & Wenceslao González–Manteiga & Ana Pérez–González, 2023. "Estimators for ROC curves with missing biomarkers values and informative covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 931-956, September.
    2. Dongliang Wang & Xueya Cai, 2021. "Smooth ROC curve estimation via Bernstein polynomials," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-12, May.

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