| dc.contributor.author | Erbay- Dalkılıç, Türkan | |
| dc.contributor.author | Kula- Sanlı, Kamile | |
| dc.contributor.author | Apaydın, Aysen | |
| dc.date.accessioned | 12.07.201910:49:13 | |
| dc.date.accessioned | 2019-07-11T21:55:38Z | |
| dc.date.available | 12.07.201910:49:13 | |
| dc.date.available | 2019-07-11T21:55:38Z | |
| dc.date.issued | 2014 | |
| dc.identifier.issn | 1303-5010 | |
| dc.identifier.uri | https://app.trdizin.gov.tr/makale/TWpJek56ZzFOUT09 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12513/682 | |
| dc.description.abstract | Regression analysis is investigation the relation between dependent andindependent variables. And, the degree and functional shape of this relation is determinate by regression analysis. In case that dependentvariable has outlier, the robust regression methods are proposed tomake smaller the effect of the outlier on the parameter estimates. Inthis study, an algorithm has been suggested to define the unknownparameters of regression model, which is based on ANFIS (AdaptiveNetwork based Fuzzy Inference System). The proposed algorithm, expressed the relation between the dependent and independent variablesby more than one model and the estimated values are obtained byconnected this model via ANFIS. In the solving process, the proposedmethod is not to be affected the outliers which are to exist in dependentvariable. So, to test the activity of the proposed algorithm, estimatedvalues obtained from this algorithm and some robust methods are compared. | en_US |
| dc.description.abstract | Regression analysis is investigation the relation between dependent andindependent variables. And, the degree and functional shape of this relation is determinate by regression analysis. In case that dependentvariable has outlier, the robust regression methods are proposed tomake smaller the effect of the outlier on the parameter estimates. Inthis study, an algorithm has been suggested to define the unknownparameters of regression model, which is based on ANFIS (AdaptiveNetwork based Fuzzy Inference System). The proposed algorithm, expressed the relation between the dependent and independent variablesby more than one model and the estimated values are obtained byconnected this model via ANFIS. In the solving process, the proposedmethod is not to be affected the outliers which are to exist in dependentvariable. So, to test the activity of the proposed algorithm, estimatedvalues obtained from this algorithm and some robust methods are compared. | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | İstatistik ve Olasılık | en_US |
| dc.subject | Matematik | en_US |
| dc.title | Parameter estimation by anfis where dependent variable has outlier | en_US |
| dc.type | article | en_US |
| dc.relation.journal | Hacettepe Journal of Mathematics and Statistics | en_US |
| dc.contributor.department | Kırşehir Ahi Evran Üniversitesi | en_US |
| dc.identifier.volume | 43 | en_US |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.startpage | 309 | en_US |
| dc.identifier.endpage | 322 | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US] |
