A numerical study of penalized regression
| dc.contributor.author | Yu, Han | |
| dc.contributor.examiningcommittee | Mandal, Saumen (Statistics) Gao, Jijun (Business Administration) | en_US |
| dc.contributor.supervisor | Wang, Xikui (Statistics) | en_US |
| dc.date.accessioned | 2013-08-22T19:37:17Z | |
| dc.date.available | 2013-08-22T19:37:17Z | |
| dc.date.issued | 2013-08-22 | |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | In this thesis, we review important aspects and issues of multiple linear regression, in particular on the problem of multi-collinearity. The focus is on a numerical study of different methods of penalized regression, including the ridge regression, lasso regression and elastic net regression, as well as the newly introduced correlation adjusted regression and correlation adjusted elastic net regression. We compare the performance and relative advantages of these methods. | en_US |
| dc.description.note | October 2013 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/22080 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | penalized regression | en_US |
| dc.title | A numerical study of penalized regression | en_US |
| dc.type | master thesis | en_US |