IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v134y2019icp186-210.html
   My bibliography  Save this article

A smooth nonparametric approach to determining cut-points of a continuous scale

Author

Listed:
  • Qiu, Zhiping
  • Peng, Limin
  • Manatunga, Amita
  • Guo, Ying

Abstract

The problem of determining cut-points of a continuous scale according to an established categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, or facilitating interpretations. A general analytic framework was recently proposed for assessing optimal cut-points defined based on some pre-specified criteria. However, the implementation of the existing nonparametric estimators under this framework and the associated inferences can be computationally intensive when more than a few cut-points need to be determined. To address this important issue, a smoothing-based modification of the current method is proposed and is found to substantially improve the computational speed as well as the asymptotic convergence rate. Moreover, a plug-in type variance estimation procedure is developed to further facilitate the computation. Extensive simulation studies confirm the theoretical results and demonstrate the computational benefits of the proposed method. The practical utility of the new approach is illustrated by an application to a mental health study.

Suggested Citation

  • Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.
  • Handle: RePEc:eee:csdana:v:134:y:2019:i:c:p:186-210
    DOI: 10.1016/j.csda.2018.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947318302779
    Download Restriction: Full text for ScienceDirect subscribers only.

    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. Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    2. Lausen, Berthold & Schumacher, Martin, 1996. "Evaluating the effect of optimized cutoff values in the assessment of prognostic factors," Computational Statistics & Data Analysis, Elsevier, vol. 21(3), pages 307-326, March.
    3. Lai, Chin-Ying & Tian, Lili & Schisterman, Enrique F., 2012. "Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1103-1114.
    4. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    5. Torsten Hothorn & Achim Zeileis, 2008. "Generalized Maximally Selected Statistics," Biometrics, The International Biometric Society, vol. 64(4), pages 1263-1269, December.
    6. Zhiping Qiu & Jing Qin & Yong Zhou, 2016. "Composite Estimating Equation Method for the Accelerated Failure Time Model with Length-biased Sampling Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 396-415, June.
    7. Rebecca A. Betensky & Daniel Rabinowitz, 1999. "Maximally Selected x-super-2 Statistics for k× 2 Tables," Biometrics, The International Biometric Society, vol. 55(1), pages 317-320, March.
    8. Heller, Glenn, 2007. "Smoothed Rank Regression With Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 552-559, June.
    9. Wang, Dongliang & Tian, Lili & Zhao, Yichuan, 2017. "Smoothed empirical likelihood for the Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 1-10.
    Full references (including those not matched with items on IDEAS)

    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:134:y:2019:i:c:p:186-210. See general information about how to correct material in RePEc.

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

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.