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Sparse Ridge Fusion For Linear Regression
| Title: | Sparse Ridge Fusion For Linear Regression. |
 48 views 26 downloads |
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| Name(s): |
Mahmood, Nozad, Author Maboudou, Edgard, Committee Chair Schott, James, Committee Member Uddin, Nizam, Committee Member , Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2013 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | For a linear regression, the traditional technique deals with a case where the number of observations n more than the number of predictor variables p (n(>)p). In the case n(<)p, the classical method fails to estimate the coefficients. A solution of this problem in the case of correlated predictors is provided in this thesis. A new regularization and variable selection is proposed under the name of Sparse Ridge Fusion (SRF). In the case of highly correlated predictor , the simulated examples and a real data show that the SRF always outperforms the lasso, elastic net, and the S-Lasso, and the results show that the SRF selects more predictor variables than the sample size n while the maximum selected variables by lasso is n size. | |
| Identifier: | CFE0005031 (IID), ucf:49997 (fedora) | |
| Note(s): |
2013-12-01 M.S. Sciences, Statistics Masters This record was generated from author submitted information. |
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| Subject(s): | Lasso -- Coordinate Descent -- Elastic Net -- Smooth Lasso -- Sparsity -- Collinearity -- High-Dimensional Data -- Variable Selection. | |
| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0005031 | |
| Restrictions on Access: | public 2013-12-15 | |
| Host Institution: | UCF |
