Performance Assessment of some Existing and New Ridge Regression Estimators
| dc.contributor.author | Himmad khan | |
| dc.date.accessioned | 2025-07-31T11:06:25Z | |
| dc.date.available | 2025-07-31T11:06:25Z | |
| dc.date.issued | 2017-10-16 | |
| dc.description.abstract | An important assumption of the multiple linear regression model is that the regressors should be independent, which is violated in practice. Due to violation of this assumption, the problem of multicollinearity occurs and results into unreliable statistical inference for the model parameters. To cope this, several biased estimation techniques, including ridge regression, have been proposed in the literature. In ridge regression, the key problem is to estimate the ridge parameter and there are several methods in the literature for this purpose. In this thesis, we propose six different shrinkage estimators, named as HMS1 to HMS6 and further compare them to some existing methods, like HK, KMS, KSM, KMED and KGM. We use Monte Carlo simulations to assess the performance of different estimators assuming the mean square error as a performance comparison criterion. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/4232 | |
| dc.language.iso | en_US | |
| dc.publisher | UMT.Lahore | |
| dc.title | Performance Assessment of some Existing and New Ridge Regression Estimators | |
| dc.type | Thesis |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- Performance Assessment of some Existing and New Ridge Regression Estimators.docx
- Size:
- 579.43 KB
- Format:
- Microsoft Word XML
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: