Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9916
Title: A fast algorithm for non-negativity model selection
Authors: Gatu, Cristian 
Kontoghiorghes, Erricos John 
Keywords: Branch-and-bound algorithms;Subset selection;Non-negative least squares
Category: Economics and Business
Field: Social Sciences
Issue Date: 1-Apr-2013
Publisher: Springer
Source: Statistics and Computing, 2013, Volume 23, Issue 3, Pages 403-411
metadata.dc.doi: 10.1007/s11222-012-9318-8
Abstract: An efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniques for computing the best subset regression. The aim is to filling a gap in computationally tractable solutions to the non-negative least squares problem and model selection. The proposed method is illustrated with a real dataset. Experimental results on real and artificial random datasets confirm the computational efficacy of the new strategy and demonstrates its ability to solve large model selection problems that are subject to non-negativity constrains.
URI: http://ktisis.cut.ac.cy/handle/10488/9916
ISSN: 09603174
Rights: © 2012 Springer Science+Business Media, LLC.
Type: Article
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