IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v30y2018i3p640-661.html
   My bibliography  Save this article

A nonparametric time-varying coefficient model for panel count data

Author

Listed:
  • Huadong Zhao
  • Wanzhu Tu
  • Zhangsheng Yu

Abstract

In this research, we describe a nonparametric time-varying coefficient model for the analysis of panel count data. We extend the traditional panel count data models by incorporating B-splines estimates of time-varying coefficients. We show that the proposed model can be implemented using a nonparametric maximum pseudo-likelihood method. We further examine the theoretical properties of the estimators of model parameters. The operational characteristics of the proposed method are evaluated through a simulation study. For illustration, we analyse data from a study of childhood wheezing, and describe the time-varying effect of an inflammatory marker on the risk of wheezing.

Suggested Citation

  • Huadong Zhao & Wanzhu Tu & Zhangsheng Yu, 2018. "A nonparametric time-varying coefficient model for panel count data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(3), pages 640-661, July.
    • Handle: RePEc:taf:gnstxx:v:30:y:2018:i:3:p:640-661
    DOI: 10.1080/10485252.2018.1458982
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2018.1458982
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2018.1458982?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weiwei Wang & Yijun Wang & Xiaobing Zhao, 2022. "Semiparametric analysis of multivariate panel count data with nonlinear interactions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 89-115, January.
    2. Guo, Yuanyuan & Sun, Dayu & Sun, Jianguo, 2022. "Inference of a time-varying coefficient regression model for multivariate panel count data," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    3. Yijun Wang & Weiwei Wang, 2021. "Quantile estimation of semiparametric model with time-varying coefficients for panel count data," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-18, December.
    4. Yang Wang & Zhangsheng Yu, 2022. "A kernel regression model for panel count data with nonparametric covariate functions," Biometrics, The International Biometric Society, vol. 78(2), pages 586-597, June.
    5. Fei Qin & Zhangsheng Yu, 2021. "Penalized spline estimation for panel count data model with time-varying coefficients," Computational Statistics, Springer, vol. 36(4), pages 2413-2434, December.

    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:30:y:2018:i:3:p:640-661. 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.