Abstract
In this paper, we discuss the problem of regression analysis in a fuzzy domain. By considering an iterative Weighted Least Squares estimation approach, we propose a general linear regression model for studying the dependence of a general class of fuzzy response variable, i.e., \(LR_2\) fuzzy variable or trapezoidal fuzzy variable,on a set of crisp or \(LR_2\) fuzzy explanatory variables. We also show some theoretical properties and a suitable generalization of the determination coefficient in order to investigate the goodness of fit of the regression model. Furthermore, we discuss some theoretical issues and an assessment of imprecision of the regression function. Finally, we suggest a robust version of the fuzzy regression model based on the Least Median Squares estimation approach which is able to neutralize and/or smooth the disruptive effects of possible crisp or fuzzy outliers in the estimation process. A simulation study and two empirical applications are presented.
Similar content being viewed by others
Notes
\(\mathbf {M}_1,\,\mathbf {M}_2,\,\mathbf {L},\,\text { and } \mathbf {R}\) also contain a vector of ones, related to the intercepts of the model.
References
Anand Raj, P., Nagesh Kumar, D.: Ranking alternatives with fuzzy weights using maximizing set and minimizing set. Fuzzy Sets Syst. 105(3), 365–375 (1999)
Blanco-Fernández, A., Colubi, A., García-Bárzana, M.: A set arithmetic-based linear regression model for modelling interval-valued responses through real-valued variables. Inf. Sci. 247(20), 109–122 (2013)
Blanco-Fernández, A., Casals, M.R., Colubi, A., Corral, N., García-Bárzana, M., Gil, M.A., González-Rodríguez, G., López, M.T., Lubiano, M.A., Montenegro, M., Ramos-Guajardo, A.B., de la Rosa de Sáa, S., Sinova, B.: A distance-based statistical analysis of fuzzy number-valued data. Int. J. Approx. Reason. doi:10.1016/j.ijar.2013.09.020 (2014)
Celmiņš, A.: Least squares model fitting to fuzzy vector data. Fuzzy Sets Syst. 22(3), 245–269 (1987)
Chang, O.Y.H., Ayyub, M.B.: Fuzzy regression methods—a comparative assessment. Fuzzy Sets Syst. 119(2), 187–203 (2001)
Chang, P.T., Stanley Lee, E.: A generalized fuzzy weighted least-squares regression. Fuzzy Sets Syst. 82(3), 289–298 (1996)
Colubi, A., Coppi, R., D’Urso, P., Gil, M.A.: Statistics with fuzzy random variable. Metron LXV(3), 277–303 (2007)
Coppi, R., D’Urso, P.: Fuzzy k-mean clustering models for triangular fuzzy time trajectories. Stat. Methods Appl. 11, 21–24 (2002)
Coppi, R., D’Urso, P.: Regression analysis with fuzzy informational paradigm: a least-squares approach using membership function information. Int. J. Pure Appl. Math. 8, 279–306 (2003)
Coppi, R., D’Urso, P., Giordani, P., Santoro, A.: Least squares estimation of a linear regression model with LR fuzzy response. Comput. Stat. Data Anal. 51(1), 267–286 (2006)
Diamond, P.: Fuzzy least squares. Inf. Sci. 46(3), 141–157 (1988)
Diamond, P., Tanaka, H.: Fuzzy regression analysis. In: Slowinski, R. (ed.) Fuzzy Sets in Decision Analysis, Operations Research and Statistics, pp. 349–387. Kluwer Academic Publishers, Massachusetts (1998)
Dubois, D., Prade, H.: Possibility Theory. Plenum Press, New York (1988)
D’Urso, P.: Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data. Comput. Stat. Data Anal. 42(1), 47–72 (2003)
D’Urso, P., Gastaldi, T.: A least-squares approach to fuzzy linear regression analysis. Comput. Stat. Data Anal. 34(4), 427–440 (2000)
D’Urso, P., Giordani, P.: Fitting of fuzzy linear regression models with multivariate response. Int. Math. J. 3(6), 655–664 (2003)
D’Urso, P., Santoro, A.: Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable. Comput. Stat. Data Anal. 51(1), 287–313 (2006)
D’Urso, P., Santoro, A.: Goodness of fit and variable selection in the fuzzy multiple linear regression. Fuzzy Sets Syst. 157, 2627–2647 (2006)
D’Urso, P., Massari, R., Santoro, A.: A class of fuzzy clusterwise regression models. Inf. Sci. 180(24), 4737–4762 (2010)
D’Urso, P., Massari, R., Santoro, A.: Robust fuzzy regression analysis. Inf. Sci. 181(19), 4154–4174 (2011)
Ferraro, M.B., Coppi, R., González Rodríguez, G., Colubi, A.: A linear regression model for imprecise response. Int. J. Approx. Reason. 51(7), 759–770 (2010)
González-Rodríguez, G., Blanco, Á., Corral, N., Colubi, A.: Least squares estimation of linear regression models for convex compact random sets. Adv. Data Anal. Classif. 1(1), 67–81 (2007)
González-Rodríguez, G., Blanco, Á., Colubi, A., Lubiano, M.A.: Estimation of a simple linear regression model for fuzzy random variables. Fuzzy Sets Syst. 160(3), 357–370 (2009)
Kim, K.J., Moskowitz, H., Koksalan, M.: Fuzzy versus statistical linear regression. Eur. J. Oper. Res. 92(2), 417–434 (1996)
Körner, R., Näther, W.: Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates. Inf. Sci. 109(1–4), 95–118 (1998)
Krätschmer, V.: Least squares estimation in linear regression models with vague concepts. In: Lopez-Diaz, M., Gil, M., Grzegorzewski, P., Hryniewicz, P., Lawry, J. (eds.) Soft Methodology and Random Information Systems, pp. 407–414. Springer, Heidelberg (2004)
Lawson, C.L., Hanson, R.J.: Solving least squares problems. SIAM, Philadelphia (1995)
Ma, M., Friedman, M., Kandel, A.: General fuzzy least squares. Fuzzy Sets Syst. 88(1), 107–118 (1997)
Näther, W.: On random fuzzy variables of second order and their application to linear statistical inference with fuzzy data. Metrika 51(3), 201–221 (2000)
Rousseeuw, P.J.: Least median of squares regression. J. Am. Stat. Assoc. 79(388), 871–880 (1984)
Rousseeuw, P.J., Leroy, A.M.: Robust regression and outlier detection, vol. 589. Wiley, New York (2005)
Sinova, B., Colubi, A., Gil, M., et al.: Interval arithmetic-based simple linear regression between interval data: discussion and sensitivity analysis on the choice of the metric. Inf. Sci. 199, 109–124 (2012)
Tanaka, H., Watada, J.: Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst. 27(3), 275–289 (1988)
Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12(6), 903–907 (1982)
Wu, H.C.: Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data. Comput. Stat. Data Anal. 42(1), 203–217 (2003)
Wünsche, A., Naether, W.: Least-squares fuzzy regression with fuzzy random variables. Fuzzy Sets Syst. 130(1), 43–50 (2002)
Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluver Academic Press, Norwell (2011)
Author information
Authors and Affiliations
Corresponding author
Appendix: Iterative solutions of the \(LR_2\) output–\(LR_2\) inputs regression model
Appendix: Iterative solutions of the \(LR_2\) output–\(LR_2\) inputs regression model
By substituting in (7) the expressions (5a)–(5d), and by putting the first partial derivatives with respect to each coefficient equal to zero, the following iterative solutions are obtained.
Rights and permissions
About this article
Cite this article
D’Urso, P., Massari, R. Weighted Least Squares and Least Median Squares estimation for the fuzzy linear regression analysis. METRON 71, 279–306 (2013). https://doi.org/10.1007/s40300-013-0025-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40300-013-0025-9