A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model

A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model In this paper we present a crisp-input/fuzzy-output regression model based on the rationale of generalized maximum entropy (GME) method. The approach can be used in several situations in which one have to handle with particular problems, such as small samples, ill-posed design matrix (e.g., due to the multicollinearity), estimation problems with inequality constraints, etc. After having described the GME-fuzzy regression model, we consider an economic case study in which the features provided from GME approach are evaluated. Moreover, we also perform a sensitivity analysis on the main results of the case study in order to better evaluate some features of the model. Finally, some critical points are discussed together with suggestions for further works. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model

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Springer Netherlands
Copyright © 2013 by Springer Science+Business Media Dordrecht
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
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