IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v80y2018i4p817-836.html
   My bibliography  Save this article

Asymptotic properties and information criteria for misspecified generalized linear mixed models

Author

Listed:
  • Dalei Yu
  • Xinyu Zhang
  • Kelvin K. W. Yau

Abstract

The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback–Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate models are misspecified. The model selection consistency of the model selection procedure is also established when the true data‐generating procedure lies within the set of candidate models. Simulation experiments confirm the effectiveness of the method proposed. The use of the criterion for model selection is illustrated through an analysis of the European Currency Opinion Survey data.

Suggested Citation

  • Dalei Yu & Xinyu Zhang & Kelvin K. W. Yau, 2018. "Asymptotic properties and information criteria for misspecified generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 817-836, September.
    • Handle: RePEc:bla:jorssb:v:80:y:2018:i:4:p:817-836
    DOI: 10.1111/rssb.12270
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssb.12270
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssb.12270?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
    ---><---

    Citations

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


    Cited by:

    1. Benjamin Säfken & Thomas Kneib, 2020. "Conditional covariance penalties for mixed models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 990-1010, September.

    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:jorssb:v:80:y:2018:i:4:p:817-836. 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: https://edirc.repec.org/data/rssssea.html .

    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.