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https://hdl.handle.net/20.500.14279/9916
Τίτλος: | A fast algorithm for non-negativity model selection | Συγγραφείς: | Gatu, Cristian Kontoghiorghes, Erricos John |
metadata.dc.contributor.other: | Κοντογιώργης, Ερρίκος | Major Field of Science: | Social Sciences | Field Category: | Economics and Business | Λέξεις-κλειδιά: | Branch-and-bound algorithms;Subset selection;Non-negative least squares | Ημερομηνία Έκδοσης: | 2013 | Πηγή: | Statistics and Computing, 2013, vol. 23, pp. 403-411 | Volume: | 23 | Start page: | 403 | End page: | 411 | Περιοδικό: | Statistics and Computing | Περίληψη: | 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: | https://hdl.handle.net/20.500.14279/9916 | ISSN: | 09603174 | DOI: | 10.1007/s11222-012-9318-8 | Rights: | © Springer | Type: | Article | Affiliation: | Cyprus University of Technology University of Nicosia |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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