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    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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    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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