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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
http://eprints.utm.my/id/eprint/48703/
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spelling 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
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
_version_ 1672610457805914112
score 13.160551

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