IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/y22y2022i1p158-194.html
   My bibliography  Save this article

Analyzing coarsened categorical data with or without probabilistic information

Author

Listed:
  • Werner Vach

    (University of Basel)

  • Cornelia Alder

    (University of Basel)

  • Jorge Rivera

    (University of Basel)

Abstract

In some applications, only a coarsened version of a categorical outcome variable can be observed. Parametric inference based on the maximum likelihood approach is feasible in principle, but it cannot be covered computationally by standard software tools. In this article, we present two commands facilitating maximum likelihood estimation in this situation for a wide range of parametric models for categorical outcomes—in the cases both of a nominal and an ordinal scale. In particular, the case of probabilistic information about the possible values of the outcome variable is also covered. Two examples motivating this scenario are presented and analyzed.

Suggested Citation

  • Werner Vach & Cornelia Alder & Jorge Rivera, 2022. "Analyzing coarsened categorical data with or without probabilistic information," Stata Journal, StataCorp LP, vol. 22(1), pages 158-194, March.
  • Handle: RePEc:tsj:stataj:y:22:y:2022:i:1:p:158-194
    DOI: 10.1177/1536867X221083902
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0668/
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1177/1536867X221083902
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1536867X221083902?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
    ---><---

    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:tsj:stataj:y:22:y:2022:i:1:p:158-194. 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: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.com/ .

    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.