IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v33y2021i3-4p435-463.html
   My bibliography  Save this article

Symmetric smoothed bootstrap methods for ranked set samples

Author

Listed:
  • Hikaru Yamaguchi
  • Hidetoshi Murakami

Abstract

Ranked set sampling (RSS) is a data collection scheme that usually yields more efficient estimators when a measurement of the variable of interest is difficult or expensive to obtain, but sampling units can be ordered easily without actual quantification. We suggest several natural methods for obtaining smoothed bootstrap samples from each row of RSS data, and show the consistency of these methods for a location parameter. Our results hold even if the ranking is imperfect. Furthermore, we propose an optimal bandwidth that minimises the asymptotic mean integrated squared error of the RSS-based kernel cumulative distribution estimator. Then, we use simulations to verify the accuracy of the percentile confidence interval for the population mean for each bootstrap method. Lastly, we apply the smoothed bootstrap method to the test of symmetry.

Suggested Citation

  • Hikaru Yamaguchi & Hidetoshi Murakami, 2021. "Symmetric smoothed bootstrap methods for ranked set samples," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 33(3-4), pages 435-463, October.
    • Handle: RePEc:taf:gnstxx:v:33:y:2021:i:3-4:p:435-463
    DOI: 10.1080/10485252.2021.1971226
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2021.1971226
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2021.1971226?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:gnstxx:v:33:y:2021:i:3-4:p:435-463. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GNST20 .

    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.