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A fuzzy regression approach using Bernstein polynomials for the spreads: Computational aspects and applications to economic models

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  • Roldán López de Hierro, Antonio Francisco
  • Martínez-Moreno, Juan
  • Aguilar Peña, Concepción
  • Roldán López de Hierro, Concepción

Abstract

An important research topic in applied statistics consists of determining the relationship between several variables through a regression function. Recently, fuzzy regression analysis has become important to deal with fuzzy data and vague information captured from the real world. When we are modeling relationships between an imprecise response and several real exploratory variables, one of the main difficulties is to guarantee the condition of non-negativity of the spreads. In this paper, due to their ease of implementation, continuous differentiability, and theoretical properties, Bernstein polynomials are used to develop a fuzzy regression procedure which guarantees this condition. We demonstrate the applicability and effectiveness of our method through the analysis of real data and comparisons with existing methodologies.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:matcom:v:128:y:2016:i:c:p:13-25
    DOI: 10.1016/j.matcom.2016.03.012
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    References listed on IDEAS

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    1. Michel Grabisch & Jean-Luc Marichal & Radko Mesiar & Endre Pap, 2011. "Aggregation functions: Means," Post-Print hal-00539028, HAL.
    2. Coppi, Renato & D'Urso, Pierpaolo & Giordani, Paolo & Santoro, Adriana, 2006. "Least squares estimation of a linear regression model with LR fuzzy response," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 267-286, November.
    3. S. McKay Curtis & Sujit K. Ghosh, 2011. "A variable selection approach to monotonic regression with Bernstein polynomials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 961-976, February.
    4. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
    5. Michel Grabisch & Jean-Luc Marichal & Radko Mesiar & Endre Pap, 2011. "Aggregation functions: construction methods, conjunctive, disjunctive and mixed classes," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00539032, HAL.
    6. Wang, J. & Ghosh, S.K., 2012. "Shape restricted nonparametric regression with Bernstein polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2729-2741.
    7. 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.
    8. Osman, Muhtarjan & Ghosh, Sujit K., 2012. "Nonparametric regression models for right-censored data using Bernstein polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 559-573.
    9. 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.
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