IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v212y2025ics0167947325001094.html
   My bibliography  Save this article

Bayesian selection approach for categorical responses via multinomial probit models

Author

Listed:
  • Chu, Chi-Hsiang
  • Lee, Kuo-Jung
  • Hsu, Chien-Chin
  • Chen, Ray-Bing

Abstract

A multinomial probit model is proposed to examine a categorical response variable, with the main objective being the identification of the influential variables in the model. To this end, a Bayesian selection technique using two hierarchical indicators is employed. The first indicator denotes a variable's relevance to the categorical response, and the subsequent indicator relates to the variable's importance at a specific categorical level, which aids in assessing its impact at that level. The selection process relies on the posterior indicator samples generated through an MCMC algorithm. The efficacy of our Bayesian selection strategy is demonstrated through both simulation and an application to a real-world example.

Suggested Citation

  • Chu, Chi-Hsiang & Lee, Kuo-Jung & Hsu, Chien-Chin & Chen, Ray-Bing, 2025. "Bayesian selection approach for categorical responses via multinomial probit models," Computational Statistics & Data Analysis, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:csdana:v:212:y:2025:i:c:s0167947325001094
    DOI: 10.1016/j.csda.2025.108233
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947325001094
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2025.108233?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:csdana:v:212:y:2025:i:c:s0167947325001094. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.