Gatu, C. and Kontoghiorghes, Erricos (2003) Parallel algorithms for computing all possible subset regression models using the QR decomposition. Parallel Computing 29 (4), pp. 505-521. ISSN 0167-8191.
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.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 11 May 2021 21:50 |
| Last Modified: | 09 Aug 2023 12:50 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/44238 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
