Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1989
Title: | Parallel algorithms for computing all possible subset regression models using the qr decomposition |
Authors: | Gatu, Cristian Kontoghiorghes, Erricos John |
metadata.dc.contributor.other: | Κοντογιώργης, Έρρικος Γιάννης |
Major Field of Science: | Social Sciences |
Field Category: | Economics and Business |
Keywords: | Parallel algorithms;Mathematical models;Parallel algorithms;Regression analysis |
Issue Date: | Apr-2003 |
Source: | Parallel Computing, 2003, vol. 29, no. 4, pp. 505-521 |
Volume: | 29 |
Issue: | 4 |
Start page: | 505 |
End page: | 521 |
Journal: | Parallel Computing |
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: | https://hdl.handle.net/20.500.14279/1989 |
ISSN: | 1678191 |
DOI: | 10.1016/S0167-8191(03)00019-X |
Rights: | © Elsevier |
Type: | Article |
Affiliation : | Université de Neuchâtel |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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