| dc.contributor.author | Akinci, Eylem Deniz | |
| dc.contributor.author | Akbilgic, Oğuz | |
| dc.date.accessioned | 2021-03-04T11:15:10Z | |
| dc.date.available | 2021-03-04T11:15:10Z | |
| dc.date.issued | 2009 | |
| dc.identifier.citation | Akbilgic O., Akinci E. D. , "A Novel Regression Approach: Least Squares Ratio", COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.38, sa.9, ss.1539-1545, 2009 | |
| dc.identifier.issn | 0361-0926 | |
| dc.identifier.other | av_716412cf-92d7-4611-a69e-050e1c5026cf | |
| dc.identifier.other | vv_1032021 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12627/78112 | |
| dc.identifier.uri | https://doi.org/10.1080/03610920802455076 | |
| dc.description.abstract | Regression Analysis (RA) is one of the frequently used tool for forecasting. The Ordinary Least Squares (OLS) Technique is the basic instrument of RA and there are many regression techniques based on OLS. This paper includes a new regression approach, called Least Squares Ratio (LSR), and comparison of OLS and LSR according to mean square errors of estimation of theoretical regression parameters (mse ss) and dependent value (mse y). | |
| dc.language.iso | eng | |
| dc.subject | İSTATİSTİK & OLASILIK | |
| dc.subject | Matematik | |
| dc.subject | Temel Bilimler (SCI) | |
| dc.title | A Novel Regression Approach: Least Squares Ratio | |
| dc.type | Makale | |
| dc.relation.journal | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | |
| dc.contributor.department | Mimar Sinan Güzel Sanatlar Üniversitesi , , | |
| dc.identifier.volume | 38 | |
| dc.identifier.issue | 9 | |
| dc.identifier.startpage | 1539 | |
| dc.identifier.endpage | 1545 | |
| dc.contributor.firstauthorID | 82545 | |
