FUZZY ROBUST REGRESSION ANALYSIS BASED ON THE RANKING OF FUZZY SETS
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, cilt.16, sa.5, ss.663-681, 2008 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 5
- Basım Tarihi: 2008
- Doi Numarası: 10.1142/s0218488508005558
- Dergi Adı: INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.663-681
- Anahtar Kelimeler: Robust regression, outlier, fuzzy regression, membership function, OM index, INPUT-OUTPUT DATA, LINEAR-REGRESSION, LEAST-SQUARES, MODEL, OUTLIERS
- Ankara Üniversitesi Adresli: Evet
Özet
Since fuzzy linear regression was introduced by Tanaka et al., fuzzy regression analysis has been widely studied and applied invarious areas. Diamond proposed the fuzzy least squares method to eliminate disadvantages in the Tanaka et al method. In this paper, we propose a modified fuzzy leasts quares regression analysis. When independent variables are crisp, the dependent variable is a fuzzy number and outliers are present in the dataset. In the proposed method, the residuals are ranked as the comparison of fuzzy sets, and the weight matrix is defined by the membership function of the residuals. To illustrate how the proposed method is applied, two examples are discussed and compared in methods from the literature. Results from the numerical examples using the proposed method give good solutions.