IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-7908-2413-1_7.html
   My bibliography  Save this book chapter

Data Augmentation and MCMC for Binary and Multinomial Logit Models

In: Statistical Modelling and Regression Structures

Author

Listed:
  • Sylvia Frühwirth-Schnatter

    (Johannes-Kepler-Universität Linz, Institut für Angewandte Statistik)

  • Rudolf Frühwirth

    (Institut für Hochenergiephysik der Österreichischen Akademie der Wissenschaften)

Abstract

The paper introduces two new data augmentation algorithms for sampling the parameters of a binary or multinomial logit model from their posterior distribution within a Bayesian framework. The new samplers are based on rewriting the underlying random utility model in such away that only differences of utilities are involved. As a consequence, the error term in the logit model has a logistic distribution. If the logistic distribution is approximated by a finite scale mixture of normal distributions, auxiliary mixture sampling can be implemented to sample from the posterior of the regression parameters. Alternatively, a data augmented Metropolis–Hastings algorithm can be formulated by approximating the logistic distribution by a single normal distribution. A comparative study on five binomial and multinomial data sets shows that the new samplers are superior to other data augmentation samplers and to Metropolis–Hastings sampling without data augmentation.

Suggested Citation

  • Sylvia Frühwirth-Schnatter & Rudolf Frühwirth, 2010. "Data Augmentation and MCMC for Binary and Multinomial Logit Models," Springer Books, in: Thomas Kneib & Gerhard Tutz (ed.), Statistical Modelling and Regression Structures, pages 111-132, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2413-1_7
    DOI: 10.1007/978-3-7908-2413-1_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-7908-2413-1_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.