IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v33y2024i2d10.1007_s10260-023-00740-y.html
   My bibliography  Save this article

Integrating probability and big non-probability samples data to produce Official Statistics

Author

Listed:
  • Natalia Golini

    (University of Turin)

  • Paolo Righi

    (Italian National Statistical Institute (Istat))

Abstract

This paper introduces the pseudo-calibration estimators, a novel method that integrates a non-probability sample of big size with a probability sample, assuming both samples contain relevant information for estimating the population parameter. The proposed estimators share a structural similarity with the adjusted projection estimators and the difference estimators but they adopt a different inferential approach and informative setup. The pseudo-calibration estimators can be employed when the target variable is observed in the probability sample and, in the non-probability sample, it is observed correctly, observed with error, or predicted. This paper also introduces an original application of the jackknife-type method for variance estimation. A simulation study shows that the proposed estimators are robust and efficient compared to the regression data integration estimators that use the same informative setup. Finally, a further evaluation using real data is carried out.

Suggested Citation

  • Natalia Golini & Paolo Righi, 2024. "Integrating probability and big non-probability samples data to produce Official Statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 555-580, April.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00740-y
    DOI: 10.1007/s10260-023-00740-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00740-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00740-y?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:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00740-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.