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Possibilistic linear regression analysis for fuzzy data

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Cited by:

  1. A. Nureize & J. Watada & S. Wang, 2014. "Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading," Annals of Operations Research, Springer, vol. 219(1), pages 299-315, August.
  2. Roldán López de Hierro, Antonio Francisco & Martínez-Moreno, Juan & Aguilar Peña, Concepción & Roldán López de Hierro, Concepción, 2016. "A fuzzy regression approach using Bernstein polynomials for the spreads: Computational aspects and applications to economic models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 13-25.
  3. Ali Azadeh & Mohammad Sheikhalishahi & Ali Boostani, 2014. "A Flexible Neuro-Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non-Linear Environments," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 567-582, December.
  4. 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.
  5. Pavel Škrabánek & Jaroslav Marek & Alena Pozdílková, 2021. "Boscovich Fuzzy Regression Line," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
  6. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
  7. Guo, Peijun & Tanaka, Hideo, 2006. "Dual models for possibilistic regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 253-266, November.
  8. Chung, William, 2012. "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 95(C), pages 45-49.
  9. Eduardo Conde, 2014. "A Minmax Regret Linear Regression Model Under Uncertainty in the Dependent Variable," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 573-596, February.
  10. Xianfei Yang & Xiang Yu & Hui Lu, 2020. "Dual possibilistic regression models of support vector machines and application in power load forecasting," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
  11. Hao, Peng & Guo, Junpeng, 2017. "Constrained center and range joint model for interval-valued symbolic data regression," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 106-138.
  12. Antonio Terceño & María Glòria Barberà-Mariné & Yanina Laumann, 2018. "Análisis de los coeficientes beta: evidencia en el mercado de activos chileno," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 21(3), pages 076-093, December.
  13. Sugihara, Kazutomi & Ishii, Hiroaki & Tanaka, Hideo, 2004. "Interval priorities in AHP by interval regression analysis," European Journal of Operational Research, Elsevier, vol. 158(3), pages 745-754, November.
  14. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
  15. de Andres Sanchez, Jorge & Terceno Gomez, Antonio, 2004. "Estimating a fuzzy term structure of interest rates using fuzzy regression techniques," European Journal of Operational Research, Elsevier, vol. 154(3), pages 804-818, May.
  16. Lee, Haekwan & Tanaka, Hideo, 1999. "Upper and lower approximation models in interval regression using regression quantile techniques," European Journal of Operational Research, Elsevier, vol. 116(3), pages 653-666, August.
  17. Tanaka, Hideo & Guo, Peijun, 1999. "Portfolio selection based on upper and lower exponential possibility distributions," European Journal of Operational Research, Elsevier, vol. 114(1), pages 115-126, April.
  18. 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.
  19. Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
  20. AyÅŸe Tansu, 2022. "Fuzzy Regression Analysis with a proposed model," Technium, Technium Science, vol. 4(1), pages 250-273.
  21. Tao Hong & Pu Wang, 2013. "Fuzzy interaction regression for short term load forecasting," HSC Research Reports HSC/13/14, Hugo Steinhaus Center, Wroclaw University of Technology.
  22. Azadeh, A. & Khakestani, M. & Saberi, M., 2009. "A flexible fuzzy regression algorithm for forecasting oil consumption estimation," Energy Policy, Elsevier, vol. 37(12), pages 5567-5579, December.
  23. Ankur Moitra & Dhruv Rohatgi, 2022. "Provably Auditing Ordinary Least Squares in Low Dimensions," Papers 2205.14284, arXiv.org, revised Jun 2022.
  24. Azadeh, A. & Saberi, M. & Asadzadeh, S.M. & Khakestani, M., 2011. "A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakis," Energy, Elsevier, vol. 36(12), pages 6981-6992.
  25. Tseng, Fang-Mei & Lin, Lin, 2005. "A quadratic interval logit model for forecasting bankruptcy," Omega, Elsevier, vol. 33(1), pages 85-91, February.
  26. 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.
  27. Barros, C.P. & Emrouznejad, Ali, 2016. "Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banksAuthor-Name: Wanke, Peter," European Journal of Operational Research, Elsevier, vol. 249(1), pages 378-389.
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