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: Wan Mohd. Rosly, Wan Nur Shaziayani
Format: Thesis
Language:English
Published: 2011
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Online Access:http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf
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spelling 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
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Wan Mohd. Rosly, Wan Nur Shaziayani
Variable selection using least angle regression
description 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
qualification_level Master's degree
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
granting_institution Universiti Teknologi Malaysia, Faculty of Science
granting_department Faculty of Science
publishDate 2011
url http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf
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