Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/6777
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

SCOPUSTM   
Citations 50

9
checked on Feb 13, 2018

Page view(s) 50

167
Last Week
3
Last month
49
checked on Nov 22, 2019

Google ScholarTM

Check

Altmetric


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