IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v49y2020i13p3298-3312.html
   My bibliography  Save this article

Nonparametric analysis of recurrent gap time data

Author

Listed:
  • Pao-sheng Shen
  • Yu-Hsing Lai

Abstract

Recurrent event data are frequently encountered in longitudinal studies. In many applications, the times between successive recurrent events (gap times) are often of interest and lead to problems that have received much attention recently. In this article, using the approach of inverse probability-of-censoring weights (IPCW), we propose nonparametric estimators for the estimation of the bivariate distribution and survival functions for gap times of recurrent event data. We also consider the estimation of Kendall’s tau for two gap times by expressing it as an integral functional of the bivariate survival function. The asymptotic properties of the proposed estimators are established. Simulation studies are conducted to investigate their finite sample performance.

Suggested Citation

  • Pao-sheng Shen & Yu-Hsing Lai, 2020. "Nonparametric analysis of recurrent gap time data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(13), pages 3298-3312, July.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:13:p:3298-3312
    DOI: 10.1080/03610926.2019.1588322
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1588322
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1588322?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:lstaxx:v:49:y:2020:i:13:p:3298-3312. 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/lsta .

    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.