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

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Наслов:	Regularized least-square optimization method for variable selection in regression models
Authors:	Dimovski, Marko Stojkovska, Irena
Keywords:	linear regression, regression models, least square method, regularization, penalty functions.
Issue Date:	1-јан-2017
Publisher:	Union of Mathematicians of Macedonia
Journal:	Matematichki Bilten
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.
URI:	http://hdl.handle.net/20.500.12188/6664
Appears in Collections:	Faculty of Natural Sciences and Mathematics: Journal Articles