# 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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Main Author: Thesis English 2011 http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf No Tags, Be the first to tag this record!
id my-utm-ep.48703 uketd_dc my-utm-ep.487032020-06-17T07:30:21Z Variable selection using least angle regression 2011-05 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 http://eprints.utm.my/id/eprint/48703/ http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83844 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science Universiti Teknologi Malaysia UTM Institutional Repository English QA75 Electronic computers Computer science QA75 Electronic computers Computer science Wan Mohd. Rosly, Wan Nur Shaziayani 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 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. Thesis Master's degree Wan Mohd. Rosly, Wan Nur Shaziayani Wan Mohd. Rosly, Wan Nur Shaziayani Wan Mohd. Rosly, Wan Nur Shaziayani Variable selection using least angle regression Variable selection using least angle regression Variable selection using least angle regression Variable selection using least angle regression Variable selection using least angle regression variable selection using least angle regression Universiti Teknologi Malaysia, Faculty of Science Faculty of Science 2011 http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf 1747817455160393728