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JACIII Vol.16 No.5 pp. 576-580
doi: 10.20965/jaciii.2012.p0576
(2012)

Paper:

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Japanese Economic Analysis by Possibilistic Regression Model Building Through Possibility Maximization

Yoshiyuki Yabuuchi* and Junzo Watada**

*Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0196, Japan

Received:
December 20, 2011
Accepted:
December 31, 2011
Published:
July 20, 2012
Creative Commons License
Keywords:
fuzzy regression model, possibility grade, robustness, economic analysis
Abstract
A possibilistic regression model illustrates the potential possibilities inherent in the target system by including all data in the model. Tanaka and Guo employ exponential possibility distribution to build a model, while Inuiguchi et al. and Tajima are independently working on coinciding between the center of a possibility distribution and the center of a possibilistic regression model. Typically, samples influence and distort the shape of the model if they are far from the center of data. Yabuuchi and Watada have developed a model for describing the system possibility using the center of a possibilistic fuzzy regression model and an approach that mends the distortion of the model. The objective of this paper is to analyze the Japanese economy using our model, and to show the usefulness of our model by analysis results.
Cite this article as:
Y. Yabuuchi and J. Watada, “Japanese Economic Analysis by Possibilistic Regression Model Building Through Possibility Maximization,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.5, pp. 576-580, 2012.
Data files:
References
  1. [1] H. Tanaka and J. Watada, “Possibilistic Linear Systems and Their Application to The Linear Regression Model,” Fuzzy Sets and Systems, Vol.27, pp. 275-289, 1988.
  2. [2] H. Tanaka and P. Guo, “Possibilistic Data Analysis for Operations Research,” Phisica-Verlag, 1999.
  3. [3] H. Ishibuchi and H. Tanaka, “Interval Regression Analysis by Mixed 0-1 Integer Programming Problem,” J. of Japanese Industrial Management Association, Vol.40, No.5, pp. 312-319, 1988 (in Japanese).
  4. [4] H. Ishibuchi and H. Tanaka, “Several Formulations of Interval Regression Analysis,” Proc. of Sino-Japan Joint Meeting on Fuzzy Sets and Systems, Section B2-2, 1990.
  5. [5] Y. Yabuuchi and J. Watada, “Fuzzy Robust Regression Analysis based on a Hyperelliptic Function,” Proc. of the 4th IEEE Int. Conf. on Fuzzy Systems, pp. 1841-1848, 1995.
  6. [6] H. Lee and H. Tanaka, “Upper and lower approximation models in interval regression using regression quantile techniques,” European J. of Operational Research, Vol.116, Issue 3, pp. 653-666, 1999.
  7. [7] M. Inuiguchi, M. Sakawa, and S. Ushiro, “Mean-absolutedeviation-based fuzzy linear regression analysis by level sets automatic deduction from data,” Proc. of the Sixth IEEE Int. Conf. on Fuzzy Systems, Vol.2, pp. 829-834, 1997.
  8. [8] H. Tajima, “A Proposal of Fuzzy Regression Model,” Proc. of The Vietnam-Japan Bilateral Symposium Fuzzy Systems and Applications, pp. 383-389, 1998.
  9. [9] Y. Yabuuchi and J.Watada, “Model Building Based on Central Position for a Fuzzy Regression Model,” Proc. of Czech-Japan Seminar 2006, pp. 114-119, 2006.
  10. [10] Y. Yabuuchi and J. Watada, “Fuzzy Regression Model Building through Possibility Maximization and Its Application,” Innovative Computing, Information and Control Express Letters, Vol.4, No.2, pp. 505-510, 2010.
  11. [11] Y. Yabuuchi and J. Watada, “Fuzzy Robust Regression Model by Possibility Maximization,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.4, pp. 479-484, 2011.
  12. [12] T. Hasuike, H. Katagiri, and H. Ishii, “Multiobjective Random Fuzzy Linear Programming Problems Based on the Possibility Maximization Model,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.13, No.4, pp. 373-379, 2009.
  13. [13] A. Honda and Y. Okazaki, “Identification of Fuzzy Measures with Distorted Probability Measures,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.5, pp. 467-476, 2005.
  14. [14] Water Handbook Editorial Committee, “Water Handbook,” p. 95, Maruzen, 2003 (in Japanese).

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