IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-7908-1709-6_30.html
   My bibliography  Save this book chapter

Parameterization and estimation of path models for categorical data

In: Compstat 2006 - Proceedings in Computational Statistics

Author

Listed:
  • Tamás Rudas

    (Eötvös Loránd University, Department of Statistics, Faculty of Social Sciences)

  • Wicher Bergsma

    (London School of Economics and Political Science, Department of Statistics)

  • Renáta Németh

    (Eötvös Loránd University, Department of Statistics, Faculty of Social Sciences)

Abstract

The paper discusses statistical models for categorical data based on directed acyclic graphs (DAGs) assuming that only effects associated with the arrows of the graph exist. Graphical models based on DAGs are similar, but allow the existence of effects not directly associated with any of the arrows. Graphical models based on DAGs are marginal models and are best parameterized by using hierarchical marginal log-linear parameters. Path models are defined here by assuming that all hierarchical marginal log-linear parameters not associated with an arrow are zero, providing a parameterization with straightforward interpretation. The paper gives a brief review of log-linear, graphical and marginal models, presents a method for the maximum likelihood estimation of path models and illustrates the use of path models, with special emphasis on the interpretation of estimated parameter values, to real data.

Suggested Citation

  • Tamás Rudas & Wicher Bergsma & Renáta Németh, 2006. "Parameterization and estimation of path models for categorical data," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 383-394, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_30
    DOI: 10.1007/978-3-7908-1709-6_30
    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:978-3-7908-1709-6_30. 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.