IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v32y2020i4p814-837.html
   My bibliography  Save this article

Local polynomial regression for pooled response data

Author

Listed:
  • Dewei Wang
  • Xichen Mou
  • Xiang Li
  • Xianzheng Huang

Abstract

We propose local polynomial estimators for the conditional mean of a continuous response when only pooled response data are collected under different pooling designs. Asymptotic properties of these estimators are investigated and compared. Extensive simulation studies are carried out to compare finite sample performance of the proposed estimators under various model settings and pooling strategies. We apply the proposed local polynomial regression methods to two real-life applications to illustrate practical implementation and performance of the estimators for the mean function.

Suggested Citation

  • Dewei Wang & Xichen Mou & Xiang Li & Xianzheng Huang, 2020. "Local polynomial regression for pooled response data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(4), pages 814-837, October.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:4:p:814-837
    DOI: 10.1080/10485252.2020.1834104
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2020.1834104
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2020.1834104?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. Smith, Lisa C. & Frankenberger, Timothy R., 2022. "Recovering from severe drought in the drylands of Ethiopia: Impact of Comprehensive Resilience Programming," World Development, Elsevier, vol. 156(C).
    2. Mou, Xichen & Wang, Dewei, 2024. "Additive partially linear model for pooled biomonitoring data," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    3. Dewei Wang & Xichen Mou & Yan Liu, 2022. "Varying‐coefficient regression analysis for pooled biomonitoring," Biometrics, The International Biometric Society, vol. 78(4), pages 1328-1341, 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:32:y:2020:i:4:p:814-837. 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.