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

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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.

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Authors and Affiliations

  1. Department of History, Society and Human Studies, University of Salento, Palazzo Parlangeli, Via V.M. Stampacchia 45, 73100 , Lecce, Italy

    Enrico Ciavolino

  2. Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 84, 38068 , Rovereto, Trento, Italy

    Antonio Calcagnì

Authors
  1. Enrico Ciavolino
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  2. Antonio Calcagnì
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Correspondence to Enrico Ciavolino.

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Ciavolino, E., Calcagnì, A. A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model. Qual Quant 48, 3401–3414 (2014). https://doi.org/10.1007/s11135-013-9963-9

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  • DOI: https://doi.org/10.1007/s11135-013-9963-9

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