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|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||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|
|Appears in Collections:||Άρθρα/Articles|
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