IDEAS home Printed from https://ideas.repec.org/p/mol/ecsdps/esdp03014.html
   My bibliography  Save this paper

A possibilistic approach to latent structure analysis for symmetric fuzzy data

Author

Listed:
  • D'Urso, Pierpaolo
  • Giordani, Paolo

Abstract

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, Department of Economics.
    • Handle: RePEc:mol:ecsdps:esdp03014
    as

    Download full text from publisher

    File URL: http://web.unimol.it/progetti/repec/mol/ecsdps/ESDP03014.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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)

    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. Hou, Yongping & Wang, Bowen & Yang, Zhihua, 2011. "A method for evaluating the efficiency of PEM fuel cell engine," Applied Energy, Elsevier, vol. 88(4), pages 1181-1186, April.
    2. D'Urso, Pierpaolo & Giordani, Paolo, 2006. "A weighted fuzzy c-means clustering model for fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1496-1523, March.
    3. Debendra Kumar Mahalik, 2014. "Does Decision Changes—A Case Discussion on e-Governance Outsourcing: A Fuzzy Analysis Approach," Global Business Review, International Management Institute, vol. 15(4_suppl), pages 39-48, December.
    4. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    5. Yuan-Wei Du & Wen Zhou, 2019. "DSmT-Based Group DEMATEL Method with Reaching Consensus," Group Decision and Negotiation, Springer, vol. 28(6), pages 1201-1230, December.
    6. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.
    7. Lung-Hsin Lin & Kung-Jeng Wang, 2022. "Talent Retention of New Generations for Sustainable Employment Relationships in Work 4.0 Era—Assessment by Fuzzy Delphi Method," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    8. Büyüközkan, Gülçin & Ruan, Da, 2008. "Evaluation of software development projects using a fuzzy multi-criteria decision approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(5), pages 464-475.
    9. J. Le-Rademacher & L. Billard, 2013. "Principal component histograms from interval-valued observations," Computational Statistics, Springer, vol. 28(5), pages 2117-2138, October.
    10. Daniel Reißmann & Daniela Thrän & Alberto Bezama, 2018. "Key Development Factors of Hydrothermal Processes in Germany by 2030: A Fuzzy Logic Analysis," Energies, MDPI, vol. 11(12), pages 1-20, December.
    11. D'Urso, Pierpaolo & Giordani, Paolo, 2003. "A least squares approach to Principal Component Analysis for interval valued data," Economics & Statistics Discussion Papers esdp03013, University of Molise, Department of Economics.
    12. Kao, Chiang, 2006. "Interval efficiency measures in data envelopment analysis with imprecise data," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1087-1099, October.
    13. Shin-Liang Chan & Wann-Ming Wey & Pin-Huai Chang, 2014. "Establishing Disaster Resilience Indicators for Tan-sui River Basin in Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 115(1), pages 387-418, January.
    14. Shakhawat Chowdhury & Muhammad Al-Zahrani, 2014. "Fuzzy synthetic evaluation of treated wastewater reuse for agriculture," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(3), pages 521-538, June.
    15. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    16. Adel Hatami-Marbini & Per J. Agrell & Hirofumi Fukuyama & Kobra Gholami & Pegah Khoshnevis, 2017. "The role of multiplier bounds in fuzzy data envelopment analysis," Annals of Operations Research, Springer, vol. 250(1), pages 249-276, March.
    17. Stefan Jovčić & Petr Průša, 2021. "A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection," Mathematics, MDPI, vol. 9(21), pages 1-19, October.
    18. Seng Boon Lim & Jalaluddin Abdul Malek & Md Farabi Yussoff Md Yussoff & Tan Yigitcanlar, 2021. "Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework," Sustainability, MDPI, vol. 13(17), pages 1-31, August.
    19. Zhang, Y.M. & Huang, G.H. & He, L. & Li, Y.P., 2008. "Quality evaluation for composting products through fuzzy latent component analysis," Resources, Conservation & Recycling, Elsevier, vol. 52(10), pages 1132-1140.
    20. Coppi, Renato & D'Urso, Pierpaolo, 2006. "Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1452-1477, March.

    More about this item

    Keywords

    Latent structure analysis; symmetric fuzzy data set; possibilistic approach.;
    All these keywords.

    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:

    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:mol:ecsdps:esdp03014. 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: Claudio Lupi (email available below). General contact details of provider: https://edirc.repec.org/data/dsmolit.html .

    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.