Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1996
Title: | Seemingly unrelated regression model with unequal size observations: computational aspects |
Authors: | Foschi, Paolo Kontoghiorghes, Erricos John |
Major Field of Science: | Natural Sciences |
Field Category: | Computer and Information Sciences |
Keywords: | Least squares;Algorithms;Data reduction;Regression analysis |
Issue Date: | Nov-2002 |
Source: | Computational Statistics and Data Analysis, 2002, vol. 41, no. 1, pp. 211-229 |
Volume: | 41 |
Issue: | 1 |
Start page: | 211 |
End page: | 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. |
URI: | https://hdl.handle.net/20.500.14279/1996 |
ISSN: | 1679473 |
DOI: | 10.1016/S0167-9473(02)00146-9 |
Rights: | © Elsevier |
Type: | Article |
Affiliation : | Université de Neuchâtel |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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