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Boscovich Fuzzy Regression Line

Author

Listed:
  • Pavel Škrabánek

    (Institute of Automation and Computer Science, Brno University of Technology, Technická 2896/2, 616 69 Brno, Czech Republic)

  • Jaroslav Marek

    (Department of Mathematics and Physics, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech Republic)

  • Alena Pozdílková

    (Department of Mathematics and Physics, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech Republic)

Abstract

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.

Suggested Citation

  • Pavel Škrabánek & Jaroslav Marek & Alena Pozdílková, 2021. "Boscovich Fuzzy Regression Line," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:685-:d:522357
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    References listed on IDEAS

    as
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    4. 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.
    5. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
    6. 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.
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