Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6790
Title: Seemingly unrelated regression model with unequal size observations: computational aspects
Authors: Foschi, Paolo 
Kontoghiorghes, Erricos John 
Keywords: Least squares
Algorithms
Data reduction
Regression analysis
Issue Date: 2002
Publisher: Elsevier
Source: Computational Statistics and Data Analysis, 2002, Volume 41, Issue 1,Pages 211-229
Abstract: The computational solution of the seemingly unrelated regression model with unequal size observations is considered. Two algorithms to solve the model when treated as a generalized linear least-squares problem are proposed. The algorithms have as a basic tool the generalized QR decomposition (GQRD) and efficiently exploit the block-sparse structure of the matrices. One of the algorithms reduces the computational burden of the estimation procedure by not computing explicitly the RQ factorization of the GQRD. The maximum likelihood estimation of the model when the covariance matrix is unknown is also considered.
URI: http://ktisis.cut.ac.cy/handle/10488/6790
ISSN: 01679473
DOI: 10.1016/S0167-9473(02)00146-9
Rights: © 2002 Elsevier Science B.V. All rights reserved.
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