Regularized least-square optimization method for variable selection in regression models

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DC Field	Value	Language
dc.contributor.author	Dimovski, Marko	en_US
dc.contributor.author	Stojkovska, Irena	en_US
dc.date.accessioned	2020-01-29T10:40:29Z	-
dc.date.available	2020-01-29T10:40:29Z	-
dc.date.issued	2017-01-01	-
dc.identifier.other	UDC: 519.246:519.71	-
dc.identifier.uri	http://hdl.handle.net/20.500.12188/6664	-
dc.description.abstract	A new type of regularization in least-square optimization for variable selection in regression models is proposed. Proposed regularization is suitable for regression models with equal or at least comparable regressors’ influence. Consistency of the estimator of the regression parameter under suitable assumptions is shown. Numerical results demonstrate efficiency of the proposed regularization and its better performance compared to existing regularization methods.	en_US
dc.language.iso	en	en_US
dc.publisher	Union of Mathematicians of Macedonia	en_US
dc.relation.ispartof	Matematichki Bilten	en_US
dc.subject	linear regression, regression models, least square method, regularization, penalty functions.	en_US
dc.title	Regularized least-square optimization method for variable selection in regression models	en_US
dc.type	Journal Article	en_US
item.fulltext	With Fulltext	-
item.grantfulltext	open	-
crisitem.author.dept	Faculty of Natural Sciences and Mathematics	-
Appears in Collections:	Faculty of Natural Sciences and Mathematics: Journal Articles