Please use this identifier to cite or link to this item:
|Title:||Computational methods for modifying seemingly unrelated regressions models||Authors:||Kontoghiorghes, Erricos John||Keywords:||Least squares;Algorithms;Problem solving;Regression analysis||Issue Date:||2004||Publisher:||Elsevier||Source:||Journal of Computational and Applied Mathematics, 2004, Volume 162, Issue 1, Pages 247-261||Abstract:||Computational efficient methods for updating seemingly unrelated regressions models with new observations are proposed. A recursive algorithm to solve a series of updating problems is developed. The algorithm is based on orthogonal transformations and has as main computational tool the updated generalized QR decomposition (UGQRD). Strategies to compute the orthogonal factorizations by exploiting the block-sparse structure of the matrices are designed. The problems of adding and deleting exogenous variables from the seemingly unrelated regressions model have also been investigated. The solution of these problems utilize the strategies for computing the UGQRD.||URI:||http://ktisis.cut.ac.cy/handle/10488/6777||ISSN:||0377-0427||DOI:||http://dx.doi.org/10.1016/j.cam.2003.08.024||Rights:||© 2003 Elsevier B.V. All rights reserved.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Feb 13, 2018
Page view(s) 5055
checked on Dec 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.