Paper:
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
- [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] H. Tanaka and P. Guo, “Possibilistic Data Analysis for Operations Research,” Phisica-Verlag, 1999.
- [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] 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] 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] 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] 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] H. Tajima, “A Proposal of Fuzzy Regression Model,” Proc. of The Vietnam-Japan Bilateral Symposium Fuzzy Systems and Applications, pp. 383-389, 1998.
- [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] 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] 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] 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] 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] Water Handbook Editorial Committee, “Water Handbook,” p. 95, Maruzen, 2003 (in Japanese).
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