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
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/48703/25/WanNurShaziayaniMFS2011.pdf
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id	my-utm-ep.48703
record_format	uketd_dc
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
_version_	1747817455160393728

Variable selection using least angle regression

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