IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v47y2020i12p2159-2177.html
   My bibliography  Save this article

The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis

Author

Listed:
  • E. M. Hashimoto
  • E. M. M. Ortega
  • G. M. Cordeiro
  • A. K. Suzuki
  • M. W. Kattan

Abstract

The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.

Suggested Citation

  • E. M. Hashimoto & E. M. M. Ortega & G. M. Cordeiro & A. K. Suzuki & M. W. Kattan, 2020. "The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(12), pages 2159-2177, September.
  • Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2159-2177
    DOI: 10.1080/02664763.2019.1706725
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2019.1706725
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02664763.2019.1706725?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:japsta:v:47:y:2020:i:12:p:2159-2177. 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/CJAS20 .

    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.