Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
Linear programming with fuzzy random variable coefficients
Fuzzy Sets and Systems
Fuzziness and randomness in an optimization framework
Fuzzy Sets and Systems
Hybrid fuzzy least-squares regression analysis and its relibabilty measures
Fuzzy Sets and Systems
Formulation of linguistic regression model based on natural words
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Fundamentals of Statistics with Fuzzy Data (Studies in Fuzziness and Soft Computing)
Fundamentals of Statistics with Fuzzy Data (Studies in Fuzziness and Soft Computing)
Fuzzy regression using least absolute deviation estimators
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on BISCSE 2005 " Forging the Frontiers" Part II
Fuzzy random renewal process with queueing applications
Computers & Mathematics with Applications
Generalized theory of uncertainty (GTU)-principal concepts and ideas
Computational Statistics & Data Analysis
IEEE Transactions on Fuzzy Systems
Discussion: From imprecise to granular probabilities
Fuzzy Sets and Systems
Deregulated electricity market data representation by fuzzyregression models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
Fuzzy random chance-constrained programming
IEEE Transactions on Fuzzy Systems
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine
IEEE Transactions on Fuzzy Systems
Some properties of fuzzy random renewal processes
IEEE Transactions on Fuzzy Systems
The Approximation Method for Two-Stage Fuzzy Random Programming With Recourse
IEEE Transactions on Fuzzy Systems
Fuzzy Regression Analysis by Support Vector Learning Approach
IEEE Transactions on Fuzzy Systems
A Fuzzy Random Variable Approach to Restructuring of Rough Sets through Statistical Test
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Robust shortest path problem based on a confidence interval in fuzzy bicriteria decision making
Information Sciences: an International Journal
A fractile optimisation approach for possibilistic programming problem in fuzzy random environment
International Journal of Artificial Intelligence and Soft Computing
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In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables. In order to address regression problems in the presence of such hybrid uncertain data, fuzzy random variables are introduced in this study to serve as an integral component of regression models. A new class of fuzzy regression models that is based on fuzzy random data is built, and is called the confidence-interval-based fuzzy random regression model (CI-FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, σ-confidence intervals are constructed for fuzzy random input-output data. The CI-FRRM is established based on the σ-confidence intervals. The proposed regression model gives rise to a nonlinear programming problem that consists of fuzzy numbers or interval numbers. Since sign changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic nonlinearity of this optimizationmakes it difficult to exploit the techniques of linear programming or classical nonlinear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.