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https://hdl.handle.net/20.500.14279/2035
Τίτλος: | Computationally efficient methods for estimating the updated-observations SUR models | Συγγραφείς: | Yanev, Petko I. Kontoghiorghes, Erricos John |
Major Field of Science: | Natural Sciences | Λέξεις-κλειδιά: | Parallel algorithms;Mathematical models;Regression analysis | Ημερομηνία Έκδοσης: | Νοε-2007 | Πηγή: | Applied Numerical Mathematics, 2007, vol. 57, no. 11-12, pp. 1245-1258. | Volume: | 57 | Issue: | 11-12 | Start page: | 1245 | End page: | 1258 | Περιοδικό: | Applied Numerical Mathematics | Περίληψη: | 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 |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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