Please use this identifier to cite or link to this item: https://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
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: Nov-2002
Publisher: Elsevier
Source: Computational Statistics and Data Analysis, 2002, vol. 41, no. 1, pp. 211-229
Journal: Computational Statistics and Data Analysis 
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.
ISSN: 0167-9473
DOI: 10.1016/S0167-9473(02)00146-9
Collaboration : Institut d'informatique, Université de Neuchítel
Rights: © Elsevier
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Appears in Collections:Άρθρα/Articles

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