IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i24p8476-8498.html
   My bibliography  Save this article

Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors

Author

Listed:
  • Mohadeseh Alsadat Farzammehr
  • Mohsen Mohammadzadeh
  • Mohammad Reza Zadkarami
  • Geoffrey J. McLachlan

Abstract

Existing studies on spatial linear mixed models typically assume a normal distribution for the random error components. However, such an assumption may not be appropriate in many applications. This work relaxes the normality assumption of a generalized linear mixed model with spatial correlated by using an unrestricted multivariate skew-normal distribution, which includes the normal distribution as a special case. For parameter estimation, a Bayesian inference algorithm is developed. A simulation study and the analysis of a real data set of cigarette consumption observed between 1963 and 1992 located in 46 states of the US are conducted to compare the proposed skew normal spatial mixed model with the normal spatial mixed model.

Suggested Citation

  • Mohadeseh Alsadat Farzammehr & Mohsen Mohammadzadeh & Mohammad Reza Zadkarami & Geoffrey J. McLachlan, 2022. "Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(24), pages 8476-8498, December.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:24:p:8476-8498
    DOI: 10.1080/03610926.2021.1897843
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1897843
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1897843?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.

    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:lstaxx:v:51:y:2022:i:24:p:8476-8498. 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/lsta .

    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.