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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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Online Access: | 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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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | 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. |
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