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The fuzzy approach to statistical analysis

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  • Coppi, Renato
  • Gil, Maria A.
  • Kiers, Henk A.L.

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  • Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.
    • Handle: RePEc:eee:csdana:v:51:y:2006:i:1:p:1-14
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    1. Dubois, Didier, 2006. "Possibility theory and statistical reasoning," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 47-69, November.
    2. 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.
    3. D'Urso, Pierpaolo & Santoro, Adriana, 2006. "Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 287-313, November.
    4. Coppi, Renato & D'Urso, Pierpaolo & Giordani, Paolo & Santoro, Adriana, 2006. "Least squares estimation of a linear regression model with LR fuzzy response," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 267-286, November.
    5. Guo, Peijun & Tanaka, Hideo, 2006. "Dual models for possibilistic regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 253-266, November.
    6. Gil, Maria Angeles & Montenegro, Manuel & Gonzalez-Rodriguez, Gil & Colubi, Ana & Rosa Casals, Maria, 2006. "Bootstrap approach to the multi-sample test of means with imprecise data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 148-162, November.
    7. Giordani, Paolo & Kiers, Henk A.L., 2006. "A comparison of three methods for principal component analysis of fuzzy interval data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 379-397, November.
    8. Qiu, Weiliang & Joe, Harry, 2006. "Separation index and partial membership for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 585-603, February.
    9. Shepherd, David & Shi, Francis K.C., 2006. "Fuzzy modelling and estimation of economic relationships," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 417-433, November.
    10. Rodriguez-Muniz, Luis J. & Lopez-Diaz, Miguel & Gil, Maria Angeles, 2005. "Solving influence diagrams with fuzzy chance and value nodes," European Journal of Operational Research, Elsevier, vol. 167(2), pages 444-460, December.
    11. Nozer D. Singpurwalla & Jane M. Booker, 2004. "Membership Functions and Probability Measures of Fuzzy Sets," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 867-877, January.
    12. Lopez-Diaz, Miguel & Ralescu, Dan A., 2006. "Tools for fuzzy random variables: Embeddings and measurabilities," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 109-114, November.
    13. Grzegorzewski, Przemyslaw, 2006. "The coefficient of concordance for vague data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 314-322, November.
    14. 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.
    15. Krätschmer, Volker, 2006. "Strong consistency of least-squares estimation in linear regression models with vague concepts," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 633-654, March.
    16. Manuel Montenegro & Ana Colubi & María Rosa Casals & María Ángeles Gil, 2004. "Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 31-49, February.
    17. Hryniewicz, Olgierd, 2006. "Goodman-Kruskal [gamma] measure of dependence for fuzzy ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 323-334, November.
    18. Gulbay, Murat & Kahraman, Cengiz, 2006. "Development of fuzzy process control charts and fuzzy unnatural pattern analyses," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 434-451, November.
    19. Hebert, Pierre-Alexandre & Masson, Marie-Helene & Denoeux, Thierry, 2006. "Fuzzy multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 335-359, November.
    20. Baudrit, C. & Dubois, D., 2006. "Practical representations of incomplete probabilistic knowledge," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 86-108, November.
    21. Zadeh, Lotfi A., 2006. "Generalized theory of uncertainty (GTU)--principal concepts and ideas," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 15-46, November.
    22. Sessions, David N. & Stevans, Lonnie K., 2006. "Investigating omitted variable bias in regression parameter estimation: A genetic algorithm approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2835-2854, June.
    23. Krätschmer, Volker, 2006. "Limit distributions of least squares estimators in linear regression models with vague concepts," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1044-1069, May.
    24. Coletti, Giulianella & Scozzafava, Romano, 2006. "Conditional probability and fuzzy information," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 115-132, November.
    25. Wasito, Ito & Mirkin, Boris, 2006. "Nearest neighbours in least-squares data imputation algorithms with different missing patterns," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 926-949, February.
    26. Hathaway, Richard J. & Bezdek, James C., 2006. "Extending fuzzy and probabilistic clustering to very large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 215-234, November.
    27. Bernhard Arnold & Oke Gerke, 2003. "Testing fuzzy linear hypotheses in linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 57(1), pages 81-95, February.
    28. Groenen, P.J.F. & Winsberg, S. & Rodriguez, O. & Diday, E., 2006. "I-Scal: Multidimensional scaling of interval dissimilarities," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 360-378, November.
    29. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
    30. Ainsworth, L.M. & Dean, C.B., 2006. "Approximate inference for disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2552-2570, June.
    31. Gonzalez-Rodriguez, Gil & Colubi, Ana & Angeles Gil, Maria, 2006. "A fuzzy representation of random variables: An operational tool in exploratory analysis and hypothesis testing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 163-176, November.
    32. Doring, Christian & Lesot, Marie-Jeanne & Kruse, Rudolf, 2006. "Data analysis with fuzzy clustering methods," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 192-214, November.
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    Cited by:

    1. Ana Colubi & Renato Coppi & Pierpaolo D’urso & Maria angeles Gil, 2007. "Statistics with fuzzy random variables," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 277-303.
    2. Karel Hron & Paula Brito & Peter Filzmoser, 2017. "Exploratory data analysis for interval compositional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(2), pages 223-241, June.
    3. Naoto Yamashita & Shin-ichi Mayekawa, 2015. "A new biplot procedure with joint classification of objects and variables by fuzzy c-means clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 243-266, September.
    4. A. Blanco-Fernández & A. Ramos-Guajardo & A. Colubi, 2013. "Fuzzy representations of real-valued random variables: applications to exploratory and inferential studies," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 245-259, November.
    5. Enrico Ciavolino & Antonio Calcagnì, 2014. "A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3401-3414, November.
    6. Colubi, Ana & Gonzalez-Rodriguez, Gil, 2007. "Triangular fuzzification of random variables and power of distribution tests: Empirical discussion," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4742-4750, May.
    7. Ramos-Guajardo, Ana Belén & Lubiano, María Asunción, 2012. "K-sample tests for equality of variances of random fuzzy sets," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 956-966.
    8. Tianyu Tan & Hye Suk & Heungsun Hwang & Jooseop Lim, 2013. "Functional fuzzy clusterwise regression analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(1), pages 57-82, March.
    9. Fernando Reche & María Morales & Antonio Salmerón, 2020. "Statistical Parameters Based on Fuzzy Measures," Mathematics, MDPI, vol. 8(11), pages 1-20, November.
    10. Enrico Ciavolino & Sergio Salvatore & Antonio Calcagnì, 2014. "A fuzzy set theory based computational model to represent the quality of inter-rater agreement," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2225-2240, July.

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