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Ridge Fuzzy Regression Modelling for Solving Multicollinearity

Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
Department of Applied Mathematics, Hanyang University, Gyeonggi-do 15588, Korea
Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1572;
Received: 14 August 2020 / Revised: 5 September 2020 / Accepted: 10 September 2020 / Published: 12 September 2020
(This article belongs to the Special Issue Applications of Fuzzy Optimization and Fuzzy Decision Making)
This paper proposes an α-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting. By incorporating α-levels in the estimation procedure, we are able to construct a fuzzy ridge estimator which does not depend on the distance between fuzzy numbers. An optimized α-level estimation algorithm is selected which minimizes the root mean squares for fuzzy data. Simulation experiments and an empirical study comparing the proposed ridge fuzzy regression with fuzzy linear regression is presented. Results show that the proposed model can control the effect of multicollinearity from moderate to extreme levels of correlation between covariates, across a wide spectrum of spreads for the fuzzy response. View Full-Text
Keywords: ridge fuzzy regression; α-level estimation algorithm; fuzzy linear regression ridge fuzzy regression; α-level estimation algorithm; fuzzy linear regression
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MDPI and ACS Style

Kim, H.; Jung, H.-Y. Ridge Fuzzy Regression Modelling for Solving Multicollinearity. Mathematics 2020, 8, 1572.

AMA Style

Kim H, Jung H-Y. Ridge Fuzzy Regression Modelling for Solving Multicollinearity. Mathematics. 2020; 8(9):1572.

Chicago/Turabian Style

Kim, Hyoshin, and Hye-Young Jung. 2020. "Ridge Fuzzy Regression Modelling for Solving Multicollinearity" Mathematics 8, no. 9: 1572.

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