IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v14y2018i1p15n3.html
   My bibliography  Save this article

Nonparametric Interval Estimators for the Coefficient of Variation

Author

Listed:
  • Wang Dongliang

    (Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, USA)

  • Formica Margaret K.

    (Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NYUSA)

  • Liu Song

    (Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NYUSA)

Abstract

The coefficient of variation (CV) is a widely used scaleless measure of variability in many disciplines. However the inference for the CV is limited to parametric methods or standard bootstrap. In this paper we propose two nonparametric methods aiming to construct confidence intervals for the coefficient of variation. The first one is to apply the empirical likelihood after transforming the original data. The second one is a modified jackknife empirical likelihood method. We also propose bootstrap procedures for calibrating the test statistics. Results from our simulation studies suggest that the proposed methods, particularly the empirical likelihood method with bootstrap calibration, are comparable to existing methods for normal data and yield better coverage probabilities for nonnormal data. We illustrate our methods by applying them to two real-life datasets.

Suggested Citation

  • Wang Dongliang & Formica Margaret K. & Liu Song, 2018. "Nonparametric Interval Estimators for the Coefficient of Variation," The International Journal of Biostatistics, De Gruyter, vol. 14(1), pages 1-15, May.
    • Handle: RePEc:bpj:ijbist:v:14:y:2018:i:1:p:15:n:3
    DOI: 10.1515/ijb-2017-0041
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2017-0041
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2017-0041?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.

    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:bpj:ijbist:v:14:y:2018:i:1:p:15:n:3. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.