Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6780
Title: Parallel algorithms for computing all possible subset regression models using the qr decomposition
Authors: Gatu, Cristian 
Kontoghiorghes, Erricos John 
Keywords: Parallel algorithms
Mathematical models
Parallel algorithms
Regression analysis
Issue Date: 2003
Publisher: Elsevier
Source: Parallel Computing, 2003, Volume 29, Issue 4 SPEC., Pages 505-521
Abstract: Efficient parallel algorithms for computing all possible subset regression models are proposed. The algorithms are based on the dropping columns method that generates a regression tree. The properties of the tree are exploited in order to provide an efficient load balancing which results in no inter-processor communication. Theoretical measures of complexity suggest linear speedup. The parallel algorithms are extended to deal with the general linear and seemingly unrelated regression models. The case where new variables are added to the regression model is also considered. Experimental results on a shared memory machine are presented and analyzed.
URI: http://ktisis.cut.ac.cy/handle/10488/6780
ISSN: 01678191
DOI: 10.1016/S0167-8191(03)00019-X
Rights: © 2003 Elsevier Science B.V. All rights reserved.
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