Please use this identifier to cite or link to this item:
|Title:||A comparative study of algorithms for solving seemingly unrelated regressions models||Authors:||Belsley, David A.
Kontoghiorghes, Erricos John
|Keywords:||Regression analysis;Algorithms;Problem solving||Issue Date:||2003||Publisher:||Elsevier||Source:||Computational Statistics and Data Analysis, 2003, Volume 44, Issue 1-2, Pages 3-35||Abstract:||The computational efficiency of various algorithms for solving seemingly unrelated regressions (SUR) models is investigated. Some of the algorithms adapt known methods; others are new. The first transforms the SUR model to an ordinary linear model and uses the QR decomposition to solve it. Three others employ the generalized QR decomposition to solve the SUR model formulated as a generalized linear least-squares problem. Strategies to exploit the structure of the matrices involved are developed. The algorithms are reconsidered for solving the SUR model after it has been transformed to one of smaller dimensions.||URI:||http://ktisis.cut.ac.cy/handle/10488/6768||ISSN:||01679473
|DOI:||http://dx.doi.org/10.1016/S0167-9473(03)00028-8||Rights:||© 2003 Elsevier B.V. All rights reserved||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Feb 7, 2018
Page view(s) 2085
checked on Feb 22, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.