Please use this identifier to cite or link to this item:
|Title:||Computationally efficient methods for solving SURE models||Authors:||Kontoghiorghes, Erricos John
|Keywords:||Computational efficiency;Numerical analysis;Iterative methods;Generalized least squares estimators||Category:||Economics and Business||Field:||Social Sciences||Issue Date:||2001||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 1988, 2001, Pages 490-498 2nd International Conference on Numerical Analysis and Its Applications, NAA 2000; Rousse; Bulgaria; 11 June 2000 through 15 June 2000; Code 126839||Conference:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||Abstract:||Computationally efficient and numerically stable methods for solving Seemingly Unrelated Regression Equations (SURE) models are proposed. The iterative feasible generalized least squares estimator of SURE models where the regression equations have common exogenous variables is derived. At each iteration an estimator of the SURE model is obtained from the solution of a generalized linear least squares problem. The proposed methods, which have as a basic tool the generalized QR decomposition, are also found to be efficient in the general case where the number of linear independent regressors is smaller than the number of observations.||URI:||https://ktisis.cut.ac.cy/handle/10488/14726||ISBN:||9783540418146||ISSN:||2-s2.0-84944145246
|Rights:||© Springer Nature 2001||Type:||Conference Papers|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.