Communications for Statistical Applications and Methods

  • 제13권1호
  • /
  • Pages.125-134
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  • 2006
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Interval Regression Models Using Variable Selection

  • Choi Seung-Hoe (Department of General Studies, Hankuk Aviation University)
  • 발행 : 2006.04.01
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초록

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

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참고문헌

  1. Hwang, S.G. and Seo, Y.J. (1989). 제약부 구간 선형 회귀모델에 의한 실동시간의 견적. Journal of the Korean OR/MS Society, Vol. 14, 105-114
  2. Chen, Y. (1999), Fuzzy ranking and quadratic fuzzy regression. Computers and Mathematics with Applications, Vol. 38, 265-279 https://doi.org/10.1016/S0898-1221(99)00305-3
  3. Diamond, P. (1988), Fuzzy least squares. Information Science, Vol. 46, 141-157 https://doi.org/10.1016/0020-0255(88)90047-3
  4. Hong, D.H. and Hwang, C. (2004). Support vector Machine for Internal Regression. Proceeding of Autumn Conference on Korean Statistical Society, 67-72
  5. Hong, D.H. and Hwang, C. (2005). Internal regression analysis using quadratic loss support vector machine. IEEE Transactions on Fuzzy Systems, Vol. 13, 229-237 https://doi.org/10.1109/TFUZZ.2004.840133
  6. Inuiguchi, M., Fujita, H. and Tanino, T. (2001). Interval linear regression analysis bases on Minkowski difference, Proceeding of International Conference on Information Systems. Analysis and Synthesis, Vol. 7, 112-117
  7. Ishibuchi, H. and Tanaka, H. (1992). Fuzzy regression analysis using neural networks. Fuzzy sets and Systems, Vol. 50, 57-65
  8. Jeng, J,T., Chuang, C. and Su, S.F. (2003). Support vector interval regression networks for interval regression analysis. Fuzzy Sets and Systems, Vol. 138, 283-300 https://doi.org/10.1016/S0165-0114(02)00570-5
  9. Lee, H. and Tanaka, H. (1999). Upper and lower approximation models in interval regression using regression quantile techniques. European Journal of Operational Research, Vol. 116, 653-666 https://doi.org/10.1016/S0377-2217(98)00191-X
  10. Tanaka, H., Hayashi, I. and Watada, J. (1987). Interval regression analysis. Third Fuzzy System Symposium, 9-12
  11. Tanaka, H. and Lee, H. (1998). Interval regression analysis by quadratic programming approach. IEEE Transactions on Fuzzy Systems, Vol. 6, 473-481 https://doi.org/10.1109/91.728436