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

Enrico Ciavolino and Antonio Calcagnì ()

Quality & Quantity: International Journal of Methodology, 2014, vol. 48, issue 6, 3414 pages

Abstract: 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. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Generalized maximum entropy method; Fuzzy regression model; Economic case study; Fuzzy statistics; Sensitivity analysis (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:48:y:2014:i:6:p:3401-3414

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DOI: 10.1007/s11135-013-9963-9

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