Variable selection using least absolute shrinkage and selection operator

Least Absolute Shrinkage and Selection Operator (LASSO) and Forward Selection are variable selection method that implement in this study. The objectives of this study are to apply forward selection method in variable selection for a regression model, to apply LASSO method in variable selection for a...

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Main Author:	Mohd. Said, Rahaini
Format:	Thesis
Language:	English
Published:	2011
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/47970/25/RahainiMohdSaidMFS2011.pdf
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id	my-utm-ep.47970
record_format	uketd_dc
spelling	my-utm-ep.479702018-05-30T03:57:44Z Variable selection using least absolute shrinkage and selection operator 2011-05 Mohd. Said, Rahaini QA Mathematics Least Absolute Shrinkage and Selection Operator (LASSO) and Forward Selection are variable selection method that implement in this study. The objectives of this study are to apply forward selection method in variable selection for a regression model, to apply LASSO method in variable selection for a regression model using quadratic programming and leave one out cross validation and choosing the better model obtained from forward selection and LASSO method using least mean square error. The forward selection method implemented in the statistical package for social sciences (SPSS). Quadratic programming technique and leave one out cross validation from MATLAB software is applied to solve LASSO. The analyzed result showed forward selection and LASSO are chosen the same variable that should be included in the model. However the coefficient of the regression for both model differ. To choose between the two models, MSE is used as the criteria where the model with the smallest MSE is taken as the best model. The MSE for forward selection and LASSO are 0.4959 and 0.4765 respectively. Thus, LASSO is the better model compared to forward selection model. 2011-05 Thesis http://eprints.utm.my/id/eprint/47970/ http://eprints.utm.my/id/eprint/47970/25/RahainiMohdSaidMFS2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Mohd. Said, Rahaini Variable selection using least absolute shrinkage and selection operator
description	Least Absolute Shrinkage and Selection Operator (LASSO) and Forward Selection are variable selection method that implement in this study. The objectives of this study are to apply forward selection method in variable selection for a regression model, to apply LASSO method in variable selection for a regression model using quadratic programming and leave one out cross validation and choosing the better model obtained from forward selection and LASSO method using least mean square error. The forward selection method implemented in the statistical package for social sciences (SPSS). Quadratic programming technique and leave one out cross validation from MATLAB software is applied to solve LASSO. The analyzed result showed forward selection and LASSO are chosen the same variable that should be included in the model. However the coefficient of the regression for both model differ. To choose between the two models, MSE is used as the criteria where the model with the smallest MSE is taken as the best model. The MSE for forward selection and LASSO are 0.4959 and 0.4765 respectively. Thus, LASSO is the better model compared to forward selection model.
format	Thesis
qualification_level	Master's degree
author	Mohd. Said, Rahaini
author_facet	Mohd. Said, Rahaini
author_sort	Mohd. Said, Rahaini
title	Variable selection using least absolute shrinkage and selection operator
title_short	Variable selection using least absolute shrinkage and selection operator
title_full	Variable selection using least absolute shrinkage and selection operator
title_fullStr	Variable selection using least absolute shrinkage and selection operator
title_full_unstemmed	Variable selection using least absolute shrinkage and selection operator
title_sort	variable selection using least absolute shrinkage and selection operator
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2011
url	http://eprints.utm.my/id/eprint/47970/25/RahainiMohdSaidMFS2011.pdf
_version_	1747817276032155648

Variable selection using least absolute shrinkage and selection operator

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