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Minimum distance estimation of the binormal ROC curve

Author

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  • Alicja Jokiel-Rokita

    (Wroclaw University of Science and Technology)

  • Rafał Topolnicki

    (Wroclaw University of Science and Technology)

Abstract

The receiver operating characteristic (ROC) curve describes the performance of a diagnostic test, which classifies individuals into one of two categories. Many parametric, semiparametric and nonparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper the minimum distance estimation of the binormal ROC curve is considered. A modification of the estimator considered in the paper of Davidov and Nov (J Stat Plan Inference 142(4):872–877, 2012) and some new estimators are proposed. We compare the accuracy of the new estimators with known minimum distance estimators of the binormal ROC curve and we conclude that our estimators generally perform better than their competitors.

Suggested Citation

  • Alicja Jokiel-Rokita & Rafał Topolnicki, 2019. "Minimum distance estimation of the binormal ROC curve," Statistical Papers, Springer, vol. 60(6), pages 2161-2183, December.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:6:d:10.1007_s00362-017-0915-7
    DOI: 10.1007/s00362-017-0915-7
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    References listed on IDEAS

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    1. Hall, Peter G. & Hyndman, Rob J., 2003. "Improved methods for bandwidth selection when estimating ROC curves," Statistics & Probability Letters, Elsevier, vol. 64(2), pages 181-189, August.
    2. Kelly Zou & W. J. Hall, 2000. "Two transformation models for estimating an ROC curve derived from continuous data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 621-631.
    3. 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.
    4. Lloyd, Chris J., 2002. "Estimation of a convex ROC curve," Statistics & Probability Letters, Elsevier, vol. 59(1), pages 99-111, August.
    5. Jiezhun Gu & Subhashis Ghosal, 2008. "Strong approximations for resample quantile processes and application to ROC methodology," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(3), pages 229-240.
    6. Jing Qin, 2003. "Using logistic regression procedures for estimating receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 90(3), pages 585-596, September.
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    Cited by:

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    2. Błażej Kochański, 2022. "Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data," Risks, MDPI, vol. 10(10), pages 1-17, September.

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