IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v93y2016icp192-208.html
   My bibliography  Save this article

Modelling receiver operating characteristic curves using Gaussian mixtures

Author

Listed:
  • Cheam, Amay S.M.
  • McNicholas, Paul D.

Abstract

The receiver operating characteristic (ROC) curve is widely applied in measuring the performance of diagnostic tests. Many direct and indirect approaches have been proposed for modelling the ROC curve and, because of its tractability, the Gaussian distribution has typically been used to model both diseased and non-diseased populations. Using a Gaussian mixture model leads to a more flexible approach that better accounts for atypical data. The Monte Carlo method can be used to circumvent the absence of a closed-form for a functional form of the ROC curve. The proposed method, in which a Gaussian mixture is used in conjunction with the Monte Carlo method, performs favourably when compared to the crude binormal curve and the semi-parametric frequentist binormal ROC using the well-known LABROC procedure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:192-208
    DOI: 10.1016/j.csda.2015.04.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315001073
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2015.04.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Donald Dorfman & Edward Alf, 1968. "Maximum likelihood estimation of parameters of signal detection theory—A direct solution," Psychometrika, Springer;The Psychometric Society, vol. 33(1), pages 117-124, March.
    2. 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.
    3. 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.
    4. Jing Qin, 2003. "Using logistic regression procedures for estimating receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 90(3), pages 585-596, September.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alicja Jokiel-Rokita & Rafał Topolnicki, 2019. "Minimum distance estimation of the binormal ROC curve," Statistical Papers, Springer, vol. 60(6), pages 2161-2183, December.
    2. Zhongkai Liu & Howard D. Bondell, 2019. "Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 141-161, April.
    3. Zhang, Biao, 2006. "A semiparametric hypothesis testing procedure for the ROC curve area under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1855-1876, April.
    4. 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.
    5. Y. Huang & M. S. Pepe, 2009. "A Parametric ROC Model-Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies," Biometrics, The International Biometric Society, vol. 65(4), pages 1133-1144, December.
    6. 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.
    7. Yousef, Waleed A. & Kundu, Subrata & Wagner, Robert F., 2009. "Nonparametric estimation of the threshold at an operating point on the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4370-4383, October.
    8. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    9. 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.
    10. 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.
    11. Wang, Dan & Tian, Lili, 2017. "Parametric methods for confidence interval estimation of overlap coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 12-26.
    12. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.
    13. Gu Wen & Pepe Margaret, 2009. "Measures to Summarize and Compare the Predictive Capacity of Markers," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-49, October.
    14. 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.
    15. Wang, Suohong & Zhang, Biao, 2014. "Semiparametric empirical likelihood confidence intervals for AUC under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 101-115.
    16. Sean F. Reardon & Andrew D. Ho, 2015. "Practical Issues in Estimating Achievement Gaps From Coarsened Data," Journal of Educational and Behavioral Statistics, , vol. 40(2), pages 158-189, April.
    17. Juana-María Vivo & Manuel Franco & Donatella Vicari, 2018. "Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 683-704, September.
    18. To, Duc-Khanh & Adimari, Gianfranco & Chiogna, Monica, 2022. "Estimation of the volume under a ROC surface in presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    19. Rodríguez-Álvarez, María Xosé & Tahoces, Pablo G. & Cadarso-Suárez, Carmen & Lado, María José, 2011. "Comparative study of ROC regression techniques--Applications for the computer-aided diagnostic system in breast cancer detection," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 888-902, January.
    20. Chris J. Lloyd, 2000. "Regression Models for Convex ROC Curves," Biometrics, The International Biometric Society, vol. 56(3), pages 862-867, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:192-208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.