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Computationally efficient methods for estimating the updated-observations SUR models

Yanev, P.I. and Kontoghiorghes, Erricos J. (2007) Computationally efficient methods for estimating the updated-observations SUR models. Applied Numerical Mathematics 57 (11-12), 1245 - 1258. ISSN 0168-9274.

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Official URL: http://dx.doi.org/10.1016/j.apnum.2007.01.004

Abstract

Computational strategies for estimating the seemingly unrelated regressions model after been updated with new observations are proposed. A sequential block algorithm based on orthogonal transformations and rich in BLAS-3 operations is proposed. It exploits efficiently the sparse structure of the data matrix and the Cholesky factor of the variance–covariance matrix. A parallel version of the new estimation algorithms for two important classes of models is considered. The parallel algorithm utilizes an efficient distribution of the matrices over the processors and has low inter-processor communication. Theoretical and experimental results are presented and analyzed. The parallel algorithm is found for these classes of models to be scalable and efficient.

Item Type: Article
Keyword(s) / Subject(s): SUR model, updating, 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: 03 Aug 2011 10:47
Last Modified: 17 Apr 2013 12:21
URI: http://eprints.bbk.ac.uk/id/eprint/3999

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