Parallel algorithms for computing all possible subset regression models using the QR decomposition

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.

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Official URL: https://doi.org/10.1016/S0167-8191(03)00019-X

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

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