IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v32y2016i2p507-539n14.html
   My bibliography  Save this article

Weight Smoothing for Generalized Linear Models Using a Laplace Prior

Author

Listed:
  • Xia Xi

    (Dept. of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.)

  • Elliott Michael R.

Abstract

When analyzing data sampled with unequal inclusion probabilities, correlations between the probability of selection and the sampled data can induce bias if the inclusion probabilities are ignored in the analysis. Weights equal to the inverse of the probability of inclusion are commonly used to correct possible bias. When weights are uncorrelated with the descriptive or model estimators of interest, highly disproportional sample designs resulting in large weights can introduce unnecessary variability, leading to an overall larger mean square error compared to unweighted methods.

Suggested Citation

  • Xia Xi & Elliott Michael R., 2016. "Weight Smoothing for Generalized Linear Models Using a Laplace Prior," Journal of Official Statistics, Sciendo, vol. 32(2), pages 507-539, June.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:507-539:n:14
    DOI: 10.1515/jos-2016-0026
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jos-2016-0026
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jos-2016-0026?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:vrs:offsta:v:32:y:2016:i:2:p:507-539:n:14. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.