Comparison of some estimation methods in linear regression
| dc.contributor.author | Genç, Aşır | |
| dc.contributor.author | Tekşen, Ümran M. | |
| dc.contributor.author | Altındağ, İlkay | |
| dc.date.accessioned | 2018-04-27T11:48:41Z | |
| dc.date.available | 2018-04-27T11:48:41Z | |
| dc.date.issued | 2010 | |
| dc.description | URL: http://sjam.selcuk.edu.tr/sjam/article/view/248 | en_US |
| dc.description.abstract | In this study, we are informed about some methods as alternatives to the classical least squares methods which are used for simple linear and multiple linear regression analysis. In short, linear regression model is shown via matrix as;Y=X?+? where Y is the vector belonging to dependent variable, X is the design matrix of independent variables, ? is the parameter vector, ?is the vector belonging to error terms, so the least squares estimator of the linear regression is shown by?=(X^{?-1}X?Y) Alternative methods have emerged on the purpose of outliers' existing in observations unlike the least squares estimation, data's not providing the regression assumptions or using of the previous information about parameters as well. In the study, we are informed about the least absolute deviations regression apart from the least squares method, artificial neural networks, M-regression, the nonparametric regression and Bayesian regression. On the purpose of comparison of the methods' results, numerical results are derived by using the temperature variation data in Antalya and Fethiye regions for simple regression analysis and variables affecting the fuel percentage in crude oil for multiple regression analysis. | en_US |
| dc.identifier.citation | Genç, A., Tekşen, Ü. M., Altındağ, İ. (2010). Comparison of some estimation methods in linear regression. Selcuk Journal of Applied Mathematics, 10, 95-108. | en_US |
| dc.identifier.endpage | 108 | |
| dc.identifier.issn | 1302-7980 | en_US |
| dc.identifier.startpage | 95 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/10492 | |
| dc.identifier.volume | 10 | |
| dc.language.iso | en | en_US |
| dc.publisher | Selcuk University Research Center of Applied Mathematics | en_US |
| dc.relation.ispartof | Selcuk Journal of Applied Mathematics | en_US |
| dc.relation.publicationcategory | Makale - Kategori Belirlenecek | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | Least squares method | en_US |
| dc.subject | En küçük kareler yöntemi | en_US |
| dc.subject | Least absolute deviations regression | en_US |
| dc.subject | En az mutlak sapma gerilemesi | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Yapay sinir ağları | en_US |
| dc.subject | M-regression method | en_US |
| dc.subject | M-regresyon yöntemi | en_US |
| dc.subject | Nonparametric regression | en_US |
| dc.subject | Parametrik olmayan regresyon | en_US |
| dc.subject | Bayesian regression | en_US |
| dc.subject | Bayes gerilemesi | en_US |
| dc.title | Comparison of some estimation methods in linear regression | en_US |
| dc.type | Article | en_US |
