IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-4-431-65955-6_29.html
   My bibliography  Save this book chapter

Multivariate Regressions, Genetic Algorithms, and Information Complexity: A Three Way Hybrid

In: Measurement and Multivariate Analysis

Author

Listed:
  • Peter Bearse

    (University of North Carolina at Greensboro, Department of Economics)

  • Hamparsum Bozdogan

    (336 SMC, The University of Tennessee, Department of Statistics)

Abstract

Summary We develop a computationally feasible intelligent data mining and knowledge discovery technique to select the best subset of predictors in multivariate regression (MR) models. Our approach integrates novel statistical modeling procedures based on the information-theoretic measure of complexity (ICOMP) criterion with the genetic algorithm (GA). When ICOMP is used as the fitness function, the GA, which by itself is an extremely clever non-local optimization algorithm, becomes an intelligent statistical model selection device capable of pruning combinatorially large numbers of sub-models to obtain an optimal or near-optimal subset MR model of multivariate data. We demonstrate our approach by determining the best predictors of taste and odor in a Japanese rice wine (i.e., sake) data set.

Suggested Citation

  • Peter Bearse & Hamparsum Bozdogan, 2002. "Multivariate Regressions, Genetic Algorithms, and Information Complexity: A Three Way Hybrid," Springer Books, in: Shizuhiko Nishisato & Yasumasa Baba & Hamparsum Bozdogan & Koji Kanefuji (ed.), Measurement and Multivariate Analysis, pages 269-278, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-65955-6_29
    DOI: 10.1007/978-4-431-65955-6_29
    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:978-4-431-65955-6_29. 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.