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Efficient algorithms for estimating the general linear model

Yanev, P.I. and Kontoghiorghes, Erricos J. (2006) Efficient algorithms for estimating the general linear model. Parallel Computing 32 (2), pp. 195-204. ISSN 0167-8191.

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Official URL: http://dx.doi.org/10.1016/j.parco.2005.06.007

Abstract

Computationally efficient serial and parallel algorithms for estimating the general linear model are proposed. The sequential block-recursive algorithm is an adaptation of a known Givens strategy that has as a main component the Generalized QR decomposition. The proposed algorithm is based on orthogonal transformations and exploits the triangular structure of the Cholesky QRD factor of the variance–covariance matrix. Specifically, it computes the estimator of the general linear model by solving recursively a series of smaller and smaller generalized linear least squares problems. The new algorithm is found to outperform significantly the corresponding LAPACK routine. A parallel version of the new sequential algorithm which utilizes an efficient distribution of the matrices over the processors and has low inter-processor communication is developed. The theoretical computational complexity of the parallel algorithms is derived and analyzed. Experimental results are presented which confirm the theoretical analysis. The parallel strategy is found to be scalable and highly efficient for estimating large-scale general linear estimation problems.

Item Type: Article
Keyword(s) / Subject(s): General linear model, generalized QR decomposition, parallel algorithms
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 18 Aug 2011 08:46
Last Modified: 17 Apr 2013 12:21
URI: http://eprints.bbk.ac.uk/id/eprint/4028

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