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- DescriptionParallel Algorithms for Linear Models provides a complete and detailed account of the design, analysis and implementation of parallel algorithms for solving large-scale linear models. It investigates and presents efficient, numerically stable algorithms for computing the least-squares estimators and other quantities of interest on massively parallel systems. The mograph is in two parts. The first part consists of four chapters and deals with the computational aspects for solving linear models that have applicability in diverse areas. The remaining two chapters form the second part, which concentrates on numerical and computational methods for solving various problems associated with seemingly unrelated regression equations (SURE) and simultaneous equations models. The practical issues of the parallel algorithms and the theoretical aspects of the numerical methods will be of interest to a broad range of researchers working in the areas of numerical and computational methods in statistics and ecometrics, parallel numerical algorithms, parallel computing and numerical linear algebra. The aim of this mograph is to promote research in the interface of ecometrics, computational statistics, numerical linear algebra and parallelism.
- Author(s)Erricos John Kontoghiorghes
- PublisherSpringer-Verlag New York Inc.
- Date of Publication17/10/2012
- SubjectEconomics: Professional & General
- Series TitleAdvances in Computational Economics
- Series Part/Volume Number15
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Notebiography
- Weight320 g
- Width156 mm
- Height234 mm
- Spine10 mm
- Format DetailsTrade paperback (US)
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