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Improved methods for bandwidth selection when estimating ROC curves

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  • Hall, Peter G.
  • Hyndman, Rob J.

Abstract

The receiver operating characteristic (ROC) curve is used to describe the performance of a diagnostic test which classifies observations into two groups. We introduce new methods for selecting bandwidths when computing kernel estimates of ROC curves. Our techniques allow for interaction between the distributions of each group of observations and give substantial improvement in MISE over other proposed methods, especially when the two distributions are very different.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:64:y:2003:i:2:p:181-189
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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. Gaëlle Chagny & Claire Lacour, 2015. "Optimal adaptive estimation of the relative density," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 605-631, September.
    2. Kang, Le & Tian, Lili, 2013. "Estimation of the volume under the ROC surface with three ordinal diagnostic categories," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 39-51.
    3. Cheam, Amay S.M. & McNicholas, Paul D., 2016. "Modelling receiver operating characteristic curves using Gaussian mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 192-208.
    4. Kaushik Ghosh & Ram Tiwari, 2007. "Empirical process approach to some two-sample problems based on ranked set samples," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(4), pages 757-787, December.
    5. Rufibach Kaspar, 2012. "A Smooth ROC Curve Estimator Based on Log-Concave Density Estimates," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-29, April.
    6. Alicja Jokiel-Rokita & Rafał Topolnicki, 2019. "Minimum distance estimation of the binormal ROC curve," Statistical Papers, Springer, vol. 60(6), pages 2161-2183, December.
    7. Chang, Yuan-chin Ivan & Park, Eunsik, 2009. "Constructing the best linear combination of diagnostic markers via sequential sampling," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1921-1927, September.
    8. Elisa–María Molanes-López & Ricardo Cao, 2008. "Relative density estimation for left truncated and right censored data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(8), pages 693-720.
    9. 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.
    10. 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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    More about this item

    Keywords

    Bandwidth selection Binary classification Kernel estimator MISE ROC curve;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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