IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-94-017-3552-0_23.html
   My bibliography  Save this book chapter

Model Fitting for Multiple Variables by Minimising the Geometric Mean Deviation

In: Total Least Squares and Errors-in-Variables Modeling

Author

Listed:
  • Chris Tofallis

    (University of Hertfordshire Business School, Dept. of Statistics, Economics, Accounting and Management Systems)

Abstract

We consider the problem of fitting a linear model for a number of variables but without treating any one of these variables as special, in contrast to regression where one variable is singled out as being a dependent variable. Each of the variables is allowed to have error or natural variability but we do not assume any prior knowledge about the distribution or variance of this variability. The fitting criterion we use is based on the geometric mean of the absolute deviations in each direction. This combines variables using a product rather than a sum and so allows the method to naturally produce units-invariant models; this property is vital for law-like relationships in the natural or social sciences.

Suggested Citation

  • Chris Tofallis, 2002. "Model Fitting for Multiple Variables by Minimising the Geometric Mean Deviation," Springer Books, in: Sabine Van Huffel & Philippe Lemmerling (ed.), Total Least Squares and Errors-in-Variables Modeling, pages 261-267, Springer.
    • Handle: RePEc:spr:sprchp:978-94-017-3552-0_23
    DOI: 10.1007/978-94-017-3552-0_23
    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:978-94-017-3552-0_23. 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.