On multicollinearity in nonlinear regression models

dc.contributor.authorErkoç, Ali
dc.contributor.authorTez, Müjgân
dc.contributor.authorAkay, Kadri Ulaş
dc.date.accessioned2018-04-27T11:14:15Z
dc.date.available2018-04-27T11:14:15Z
dc.date.issued2010
dc.descriptionURL: http://sjam.selcuk.edu.tr/sjam/article/view/244en_US
dc.description.abstractRegression analysis includes many techniques for modeling and analyzing the relationship between a dependent variable and one or more independent variables. Linear and nonlinear regression models has widely used in many fields of applied science. One of the frequency problems in regression analysis is multicollinearity problem between the explanatory variables. If there is no linear (approximately linear) relationship between the regressors, they are said to be orthogonal. In the case of orthogonal variables, statistical inference on the model is quite reliable. But in real life, fully unbound variables which are explaining the dependent variable are likely to be very low. When the explanatory variables are not orthogonal, then least squares parameter estimation method will not provide a suitable convergence, and deviations from reality will ocur. For the linear model, many techniques were developed for the multicollinearity problem (Hoerl, AE (1962), Hoerl AE and Kennard RW (1968.1970)), but for nonlinear models there has not been any conclusive work yet. In this study, multicollinearity in nonlinear models will be analyzed and a remedy for the problem will be given.en_US
dc.identifier.citationErkoç, A., Tez, M., Akay, K. U. (2010). On multicollinearity in nonlinear regression models. Selcuk Journal of Applied Mathematics, 10, 65-72.en_US
dc.identifier.endpage72
dc.identifier.issn1302-7980en_US
dc.identifier.startpage65
dc.identifier.urihttps://hdl.handle.net/20.500.12395/10488
dc.identifier.volume10
dc.language.isoenen_US
dc.publisherSelcuk University Research Center of Applied Mathematicsen_US
dc.relation.ispartofSelcuk Journal of Applied Mathematicsen_US
dc.relation.publicationcategoryMakale - Kategori Belirleneceken_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectNonlinear modelsen_US
dc.subjectMulticollinearityen_US
dc.subjectRidge regressionen_US
dc.subjectMean square erroren_US
dc.subjectDoğrusal olmayan modelleren_US
dc.subjectSırt gerilemesien_US
dc.subjectOrtalama kare hatasıen_US
dc.subjectEşdüzlemliliken_US
dc.titleOn multicollinearity in nonlinear regression modelsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
Ali Erkoç, Müjgân Tez, Kadri Ulas Akay.pdf
Boyut:
123.41 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.51 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: