IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v30y2013i3p453-473.html
   My bibliography  Save this article

Optimal Quantization of the Support of a Continuous Multivariate Distribution based on Mutual Information

Author

Listed:
  • Bernard Colin
  • François Dubeau
  • Hussein Khreibani
  • Jules Tibeiro

Abstract

Based on the notion of mutual information between the components of a random vector, we construct, for data reduction reasons, an optimal quantization of the support of its probability measure. More precisely, we propose a simultaneous discretization of the whole set of the components of the random vector which takes into account, as much as possible, the stochastic dependence between them. Examples are presented. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Bernard Colin & François Dubeau & Hussein Khreibani & Jules Tibeiro, 2013. "Optimal Quantization of the Support of a Continuous Multivariate Distribution based on Mutual Information," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 453-473, October.
    • Handle: RePEc:spr:jclass:v:30:y:2013:i:3:p:453-473
    DOI: 10.1007/s00357-013-9127-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00357-013-9127-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00357-013-9127-6?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.

    References listed on IDEAS

    as
    1. Darbellay, Georges A., 1999. "An estimator of the mutual information based on a criterion for conditional independence," Computational Statistics & Data Analysis, Elsevier, vol. 32(1), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alessandro Barbiero & Asmerilda Hitaj, 2022. "Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models," Annals of Operations Research, Springer, vol. 315(2), pages 1573-1598, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    2. Kojadinovic, Ivan, 2005. "Relevance measures for subset variable selection in regression problems based on k-additive mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1205-1227, June.
    3. L. Bagnato & L. De Capitani & A. Punzo, 2016. "The Kullback–Leibler autodependogram," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2574-2594, October.
    4. Darbellay, Georges A & Wuertz, Diethelm, 2000. "The entropy as a tool for analysing statistical dependences in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 429-439.
    5. Weipan Xu & Xiaozhen Qin & Xun Li & Haohui Chen & Morgan Frank & Alex Rutherford & Andrew Reeson & Iyad Rahwan, 2021. "Developing China’s workforce skill taxonomy reveals extent of labor market polarization," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.

    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:jclass:v:30:y:2013:i:3:p:453-473. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.