IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v74y2020i2p96-111.html
   My bibliography  Save this article

Bootstrap inference using estimating equations and data that are linked with complex probabilistic algorithms

Author

Listed:
  • James Chipperfield

Abstract

Probabilistic record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a common way to enhance dimensions such as time and breadth or depth of detail. Probabilistic record linkage is not an error‐free process and link records that do not belong to the same unit. Naively treating such a linked file as if it is linked without errors can lead to biased inferences. This paper develops a method of making inference with estimating equations when records are linked using algorithms that are widely used in practice. Previous methods for dealing with this problem cannot accommodate such linking algorithms. This paper develops a parametric bootstrap approach to inference in which each bootstrap replicate involves applying the said linking algorithm. This paper demonstrates the effectiveness of the method in simulations and in real applications.

Suggested Citation

  • James Chipperfield, 2020. "Bootstrap inference using estimating equations and data that are linked with complex probabilistic algorithms," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(2), pages 96-111, May.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:2:p:96-111
    DOI: 10.1111/stan.12189
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/stan.12189
    Download Restriction: no
    File URL: https://libkey.io/10.1111/stan.12189?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
    ---><---

    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:bla:stanee:v:74:y:2020:i:2:p:96-111. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.