Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6739
Title: Computationally efficient methods for estimating the updated-observations SUR models
Authors: Yanev, Petko I. 
Kontoghiorghes, Erricos John 
Keywords: Parallel algorithms
Mathematical models
Regression analysis
Issue Date: 2007
Publisher: Elsevier
Source: Applied Numerical Mathematics, 2007, Volume 57, Issue 11-12, Pages 1245-1258
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: http://hdl.handle.net/10488/6739
ISSN: 01689274
DOI: 10.1016/j.apnum.2007.01.004
Rights: © 2007 IMACS.
Appears in Collections:Άρθρα/Articles

Files in This Item:
File Description SizeFormat 
Doc...doc23.5 kBMicrosoft WordView/Open
Show full item record

SCOPUSTM   
Citations 50

1
checked on Apr 23, 2017

WEB OF SCIENCETM
Citations 50

1
checked on Apr 16, 2017

Page view(s) 50

13
checked on Apr 29, 2017

Download(s) 50

2
checked on Apr 29, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.