IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v103y2015icp73-79.html
   My bibliography  Save this article

On shape properties of the receiver operating characteristic curve

Author

Listed:
  • Bhattacharya, Bhaskar
  • Hughes, Gareth

Abstract

We present formal definitions of two commonly observed asymmetries in a concave receiver operating characteristic curve. The main theorem of the paper proves that the Kullback–Leibler divergences between the underlying signal and noise variables are ordered based on these asymmetries. This result is true for any continuous distributions of the signal and noise variables.

Suggested Citation

  • Bhattacharya, Bhaskar & Hughes, Gareth, 2015. "On shape properties of the receiver operating characteristic curve," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 73-79.
    • Handle: RePEc:eee:stapro:v:103:y:2015:i:c:p:73-79
    DOI: 10.1016/j.spl.2015.04.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715215001133
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2015.04.003?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. Eguchi, Shinto & Copas, John, 2006. "Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2034-2040, October.
    2. Chris J. Lloyd, 2000. "Regression Models for Convex ROC Curves," Biometrics, The International Biometric Society, vol. 56(3), pages 862-867, September.
    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. 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.
    2. Ghosh, Indranil, 2023. "On the issue of convergence of certain divergence measures related to finding most nearly compatible probability distribution under the discrete set-up," Statistics & Probability Letters, Elsevier, vol. 203(C).
    3. Gallindo, Lucas, 2007. "Computation of Power Loss in Likelihood Ratio Tests for Probability Densities Extended by Lehmann Alternatives," OSF Preprints pkcnb, Center for Open Science.
    4. Jinxin Liu & Guan Wang & Tong Zhao & Li Zhang, 2017. "Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine," Energies, MDPI, vol. 10(7), pages 1-14, July.
    5. Valdevino Félix de Lima, Wenia & David Costa do Nascimento, Abraão & José Amorim do Amaral, Getúlio, 2021. "Distance-based tests for planar shape," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    6. Springborn, Michael & Sanchirico, James N., 2013. "A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 609-624.
    7. Hughes, G. & Madden, L. V., 2003. "Evaluating predictive models with application in regulatory policy for invasive weeds," Agricultural Systems, Elsevier, vol. 76(2), pages 755-774, May.
    8. Piyush Anand & Clarence Lee, 2023. "Using Deep Learning to Overcome Privacy and Scalability Issues in Customer Data Transfer," Marketing Science, INFORMS, vol. 42(1), pages 189-207, January.
    9. Boulesteix, Anne-Laure & Tutz, Gerhard, 2006. "Identification of interaction patterns and classification with applications to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 783-802, February.

    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:stapro:v:103:y:2015:i:c:p:73-79. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.