Variable selection using least angle regression
The least-angle regression (LARS) (Efrron, Hastie, Johnstone, and Tibshirani, 2004) is a technique used with the absence of data that consist of many independent variables. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LA...
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my.utm.487032020-06-17T07:30:21Z http://eprints.utm.my/id/eprint/48703/ Variable selection using least angle regression Wan Mohd. Rosly, Wan Nur Shaziayani QA75 Electronic computers. Computer science The least-angle regression (LARS) (Efrron, Hastie, Johnstone, and Tibshirani, 2004) is a technique used with the absence of data that consist of many independent variables. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients. The MATLAB programming codes are developed in order to solve the algorithms systematically and effortlessly. 2011-05 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf Wan Mohd. Rosly, Wan Nur Shaziayani (2011) Variable selection using least angle regression. Masters thesis, Universiti Teknologi Malaysia, Faculty of Science. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83844 |
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QA75 Electronic computers. Computer science Wan Mohd. Rosly, Wan Nur Shaziayani Variable selection using least angle regression |
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The least-angle regression (LARS) (Efrron, Hastie, Johnstone, and Tibshirani, 2004) is a technique used with the absence of data that consist of many independent variables. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients. The MATLAB programming codes are developed in order to solve the algorithms systematically and effortlessly. |
| format |
Thesis |
| author |
Wan Mohd. Rosly, Wan Nur Shaziayani |
| author_facet |
Wan Mohd. Rosly, Wan Nur Shaziayani |
| author_sort |
Wan Mohd. Rosly, Wan Nur Shaziayani |
| title |
Variable selection using least angle regression |
| title_short |
Variable selection using least angle regression |
| title_full |
Variable selection using least angle regression |
| title_fullStr |
Variable selection using least angle regression |
| title_full_unstemmed |
Variable selection using least angle regression |
| title_sort |
variable selection using least angle regression |
| publishDate |
2011 |
| url |
http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf http://eprints.utm.my/id/eprint/48703/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83844 |
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