IDEAS home Printed from
   My bibliography  Save this paper

A possibilistic approach to latent structure analysis for symmetric fuzzy data


  • D'Urso, Pierpaolo
  • Giordani, Paolo



In many situations the available amount of data is huge and can be intractable. When the data set is single valued, latent structure models are recognized techniques, which provide a useful compression of the information. This is done by considering a regression model between observed and unobserved (latent) fuzzy variables. In this paper, an extension of latent structure analysis to deal with fuzzy data is proposed. Our extension follows the possibilistic approach, widely used both in the cluster and regression frameworks. In this case, the possibilistic approach involves the formulation of a latent structure analysis for fuzzy data by optimization. Specifically, a non-linear programming problem in which the fuzziness of the model is minimized is introduced. In order to show how our model works, the results of two applications are given.

Suggested Citation

  • D'Urso, Pierpaolo & Giordani, Paolo, 2003. "A possibilistic approach to latent structure analysis for symmetric fuzzy data," Economics & Statistics Discussion Papers esdp03014, University of Molise, Dept. EGSeI.
  • Handle: RePEc:mol:ecsdps:esdp03014

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Cheng, Ching-Hsue & Yang, Kuo-Lung & Hwang, Chia-Lung, 1999. "Evaluating attack helicopters by AHP based on linguistic variable weight," European Journal of Operational Research, Elsevier, vol. 116(2), pages 423-435, July.
    2. Cheng, Ching-Hsue & Lin, Yin, 2002. "Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 174-186, October.
    3. D'Urso, Pierpaolo, 2003. "Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 47-72, February.
    4. Giordani, Paolo & Kiers, Henk A. L., 2004. "Principal Component Analysis of symmetric fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 519-548, April.
    5. Hougaard, Jens Leth, 1999. "Fuzzy scores of technical efficiency," European Journal of Operational Research, Elsevier, vol. 115(3), pages 529-541, June.
    6. Coppi, Renato & D'Urso, Pierpaolo, 2003. "Three-way fuzzy clustering models for LR fuzzy time trajectories," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 149-177, June.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Latent structure analysis; symmetric fuzzy data set; possibilistic approach.;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:mol:ecsdps:esdp03014. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Claudio Lupi). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.