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Dual models for possibilistic regression analysis

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  • Guo, Peijun
  • Tanaka, Hideo

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  • Guo, Peijun & Tanaka, Hideo, 2006. "Dual models for possibilistic regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 253-266, November.
    • Handle: RePEc:eee:csdana:v:51:y:2006:i:1:p:253-266
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    References listed on IDEAS

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    1. Wu, Hsien-Chung, 2003. "Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 203-217, February.
    2. Sugihara, Kazutomi & Ishii, Hiroaki & Tanaka, Hideo, 2004. "Interval priorities in AHP by interval regression analysis," European Journal of Operational Research, Elsevier, vol. 158(3), pages 745-754, November.
    3. Lee, Haekwan & Tanaka, Hideo, 1999. "Upper and lower approximation models in interval regression using regression quantile techniques," European Journal of Operational Research, Elsevier, vol. 116(3), pages 653-666, August.
    4. Guo, Peijun & Tanaka, Hideo, 2003. "Decision analysis based on fused double exponential possibility distributions," European Journal of Operational Research, Elsevier, vol. 148(3), pages 467-479, August.
    5. Kao, Chiang & Chyu, Chin-Lu, 2003. "Least-squares estimates in fuzzy regression analysis," European Journal of Operational Research, Elsevier, vol. 148(2), pages 426-435, July.
    6. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
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    Cited by:

    1. 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.
    2. Hsin-Chieh Wu & Horng-Ren Tsai & Tin-Chih Toly Chen & Keng-Wei Hsu, 2021. "Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach," Mathematics, MDPI, vol. 9(10), pages 1-17, May.
    3. Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.
    4. Xianfei Yang & Xiang Yu & Hui Lu, 2020. "Dual possibilistic regression models of support vector machines and application in power load forecasting," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    5. Maria Ferraro & Paolo Giordani, 2012. "A multiple linear regression model for imprecise information," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1049-1068, November.

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