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 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Least squares;Algorithms;Problem solving;Regression analysis
Issue Date: 1-Jan-2004
Source: Journal of Computational and Applied Mathematics, 2004, vol. 162, no. 1, pp. 247-261
Volume: 162
Issue: 1
Start page: 247
End page: 261
Journal: Journal of Computational and Applied Mathematics 
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.
ISSN: 0377-0427
DOI: 10.1016/j.cam.2003.08.024
Rights: © Elsevier
Type: Article
Affiliation : Université de Neuchâtel 
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