IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v74y1997i0p269-27610.1023-a1018978606429.html
   My bibliography  Save this article

Using non-traditional approaches to statistical classification and regression in DSS model analysis

Author

Listed:
  • David Steiger

Abstract

In a model-based decision support system, the decision maker initially has two primary tasks: finding the key parameters in the model and discovering how those key parameters, both individually and interactively, affect the solution. This paper presents an application of a non-traditional approach to statistical classification and regression to the inductive analysis of model output. Specifically, we describe the application of Ivakhnenko's group method of data handling (GMDH) to the identification of key model parameters and the discovery of a simplified polynomial metamodel, both of which frequently enhance the decision maker's understanding of the modeled environment. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • David Steiger, 1997. "Using non-traditional approaches to statistical classification and regression in DSS model analysis," Annals of Operations Research, Springer, vol. 74(0), pages 269-276, November.
    • Handle: RePEc:spr:annopr:v:74:y:1997:i:0:p:269-276:10.1023/a:1018978606429
    DOI: 10.1023/A:1018978606429
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018978606429
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018978606429?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.

    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:annopr:v:74:y:1997:i:0:p:269-276:10.1023/a:1018978606429. 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.