A fuzzy robust regression approach applied to bedload transport data

References

• Andersen, R. ( 2007). Modern Methods for Robust Regression. Thousand Oaks, CA: Sage.
   Google Scholar
• Chachi, J., Taheri, S. M. ( 2013). A least-absolutes regression model for imprecise response based on the generalized Hausdorff-metric. Journal of Uncertain Systems 7:265–276.
   Google Scholar
• Chachi, J., Taheri, S. M., Arghami, N. R. ( 2014). A hybrid fuzzy regression model and its application in hydrology engineering. Applied Soft Computing 25:149–158.
   Web of Science ®Google Scholar
• Chachi, J., Taheri, S. M. (2016). Multiple fuzzy regression model for fuzzy input-output data. Iranian Journal of Fuzzy Systems 13:63–78.
   Google Scholar
• Chachi, J., Taheri, S.M., Rezaei Pazhand, H. (2016). Suspended load estimation using L1-Fuzzy regression, L2-Fuzzy regression and MARS-Fuzzy regression models. Hydrological Sciences Journal 61(8):1489–1502.
   Google Scholar
• Chen, Y. S. ( 2001). Outliers detection and confidence interval modification in fuzzy regression. Fuzzy Sets and Systems 119:259–272.
   Web of Science ®Google Scholar
• Coppi, R., D’Urso, P., Giordani, P., Santoro, A. ( 2006). Least squares estimation of a linear regression model with LR fuzzy response. Computational Statistics & Data Analysis 51:267–286.
   Web of Science ®Google Scholar
• D’Urso, P., Massari, R., Santoro, A. ( 2011). Robust fuzzy regression analysis. Information Sciences 181:4154–4174.
   Google Scholar
• Ferraro, M. B., Coppi, R., González-Rodríguez, G., Colubi, A. ( 2010). A linear regression model for imprecise response. International Journal of Approximate Reasoning 51:759–770.
   Web of Science ®Google Scholar
• Fox, J., Weisberg, S. ( 2011). An R Companion to Applied Regression. 2nd ed. Thousand Oaks, CA: Sage Publications.
   Google Scholar
• Gladysz, B., Kuchta, D. ( 2007). Outliers detection in selected fuzzy regression models. In: Masulli, F., Mitra, S., Pasi, G., eds. Applications of Fuzzy Sets Theory: Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007. Camogli, Italy: Springer, pp. 211–218.
   Google Scholar
• Hojati, M., Bector, C. R., Smimou, K. ( 2005). A simple method for computation of fuzzy linear regression. European Journal of Operational Research 166:172–184.
   Web of Science ®Google Scholar
• Hu, Y. C. ( 2009). Functional-link nets with genetic-algorithm-based learning for robust nonlinear interval regression analysis. Neurocomputing 72:1808–1816.
   Web of Science ®Google Scholar
• Huber, P., Ronchetti, E. M. ( 2009). Robust Statistics. 2nd ed. Hoboken NJ: Wiley.
   Google Scholar
• Hung, W. L., Yang, M. S. ( 2006). An omission approach for detecting outliers in fuzzy regressions models. Fuzzy Sets and Systems 157:3109–3122.
   Web of Science ®Google Scholar
• Kula, K.ş., Apaydin, A. ( 2008). Fuzzy robust regression analysis based on the ranking of fuzzy sets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16:663–681.
   Web of Science ®Google Scholar
• Lu, J., Wang, R. ( 2009). An enhanced fuzzy linear regression model with more flexible spreads. Fuzzy Sets and Systems 160:2505–2523.
   Web of Science ®Google Scholar
• Maronna, R. A., Martin, R. D., Yohai, V. J. ( 2006). Robust Statistics: Theory and Methods. New York: John Wiley and Sons.
   Google Scholar
• Maronna, R. A., Yohai, V. J. ( 2013). Robust functional linear regression based on splines. Computational Statistics & Data Analysis 65:46–55.
   Web of Science ®Google Scholar
• Nasrabadi, E., Hashemi, S. M. ( 2008). Robust fuzzy regression analysis using neural networks. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16:579–598.
   Web of Science ®Google Scholar
• Nasrabadi, E., Hashemi, S. M., Ghatee, M. ( 2007). An LP-based approach to outliers detection in fuzzy regression analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15:441–456.
   Web of Science ®Google Scholar
• Nguyen, T. D., Welsch, R. ( 2010). Outlier detection and least trimmed squares approximation using semi-definite programming. Computational Statistics & Data Analysis 54:3212–3226.
   Web of Science ®Google Scholar
• Peters, G. ( 1994). Fuzzy linear regression with fuzzy intervals. Fuzzy Sets and Systems 63:45–55.
   Web of Science ®Google Scholar
• Rousseeuw, P. J. ( 1984). Least median of squares regression. Journal of Computational and Graphical Statistics 79:871–880.
   Google Scholar
• Rousseeuw, P. J., Leroy, A. M. ( 1987). Robust Regression and Outlier Detection. Hoboken, NJ: Wiley.
   Google Scholar
• Shibata, K., Watada, J., Yabuuchi, Y. ( 2008). A fuzzy regression approach for evaluating electric and electronics corporations. Journal of Japan Industrial Management Associations 59:58–67.
   Google Scholar
• Torabi, H., Behboodian, J. ( 2007). Fuzzy least-absolutes estimates in linear models. Communications in Statistics - Theory and Methods 36:1935–1944.
   Web of Science ®Google Scholar
• Varga, S. ( 2007). Robust estimations in classical regression models versus robust estimations in fuzzy regression models. Kybernetika 43:503–508.
   Web of Science ®Google Scholar
• Zimmermann, H. J. ( 2001). Fuzzy Set Theory and Its Applications. 4th ed. Boston, MA: Kluwer Nihoff.
   Google Scholar