Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2035
Title: | Computationally efficient methods for estimating the updated-observations SUR models | Authors: | Yanev, Petko I. Kontoghiorghes, Erricos John |
Major Field of Science: | Natural Sciences | Keywords: | Parallel algorithms;Mathematical models;Regression analysis | Issue Date: | Nov-2007 | Source: | Applied Numerical Mathematics, 2007, vol. 57, no. 11-12, pp. 1245-1258. | Volume: | 57 | Issue: | 11-12 | Start page: | 1245 | End page: | 1258 | Journal: | Applied Numerical Mathematics | 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 | URI: | https://hdl.handle.net/20.500.14279/2035 | ISSN: | 01689274 | DOI: | 10.1016/j.apnum.2007.01.004 | Rights: | © Elsevier | Type: | Article | Affiliation: | Cyprus University of Technology | Affiliation : | University of Cyprus University of Plovdiv “Paisii Hilendarski,” Bulgaria |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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