In this paper we propose a robust fuzzy linear regression model based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure. The proposed model is general enough to deal with data contaminated by outliers due to measurement errors or extracted from highly skewed or heavy tailed distributions. We also define suitable goodness of fit indices useful to evaluate the performances of the proposed model. The effectiveness of our model in reducing the outliers influence is shown by using applicative examples, based both on simulated and real data, and by a simulation study. (C) 2011 Elsevier Inc. All rights reserved.
Robust fuzzy regression analysis / D'Urso, Pierpaolo; Massari, Riccardo; Adriana, Santoro. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - 181:19(2011), pp. 4154-4174. [10.1016/j.ins.2011.04.031]
Robust fuzzy regression analysis
D'URSO, Pierpaolo;MASSARI, Riccardo;
2011
Abstract
In this paper we propose a robust fuzzy linear regression model based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure. The proposed model is general enough to deal with data contaminated by outliers due to measurement errors or extracted from highly skewed or heavy tailed distributions. We also define suitable goodness of fit indices useful to evaluate the performances of the proposed model. The effectiveness of our model in reducing the outliers influence is shown by using applicative examples, based both on simulated and real data, and by a simulation study. (C) 2011 Elsevier Inc. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
