BIROn - Birkbeck Institutional Research Online

    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.

    Full text not available from this repository.

    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: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 11 May 2021 21:50
    Last Modified: 11 May 2021 21:50
    URI: https://eprints.bbk.ac.uk/id/eprint/44238

    Statistics

    Downloads
    Activity Overview
    0Downloads
    9Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item