IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i13p4534-4552.html
   My bibliography  Save this article

Bayesian inference for the negative binomial-generalized Lindley regression model: properties and applications

Author

Listed:
  • Sirinapa Aryuyuen

Abstract

This article aims to develop a new linear model for count data, which is called the negative binomial - generalized Lindley (NB-GL) regression model. The NB-GL distribution has been proposed and applied to count data analysis, which is constructed as a mixture of the negative binomial and generalized Lindley distributions. The NB-GL distribution has the special sub-models, such as the negative binomial - Lindley, negative binomial - gamma, and negative binomial - exponential distributions. Parameters of the distribution and its regression model are estimated using a Bayesian approach. The NB-GL regression model is applied to fit real data sets. Its performance is compared with some traditional models. The results show that the generalized linear model for the NB-GL model describes the data sets better than other models.

Suggested Citation

  • Sirinapa Aryuyuen, 2023. "Bayesian inference for the negative binomial-generalized Lindley regression model: properties and applications," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(13), pages 4534-4552, July.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:13:p:4534-4552
    DOI: 10.1080/03610926.2021.1995434
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1995434
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1995434?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:52:y:2023:i:13:p:4534-4552. 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.