Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/6723
Title: Matrix strategies for computing the least trimmed squares estimation of the general linear and sur models
Authors: Hofmann, Marc H. 
Kontoghiorghes, Erricos John 
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: General linear model;Generalized linear least squares;Least trimmed squares;Seemingly unrelated regressions
Issue Date: 1-Dec-2010
Source: Computational Statistics and Data Analysis, 2010, vol. 54, no. 12, pp. 3392-3403
Volume: 54
Issue: 12
Start page: 3392
End page: 3403
Journal: Computational Statistics and Data Analysis 
Abstract: An algorithm for computing the exact least trimmed squares (LTS) estimator of the standard regression model has recently been proposed. The LTS algorithm is adapted to the general linear and seemingly unrelated regressions models with possible singular dispersion matrices. It searches through a regression tree to find the optimal estimates and has combinatorial complexity. The model is formulated as a generalized linear least squares problem. Efficient matrix techniques are employed to update the generalized residual sum of squares of a subset model. Specifically, the new algorithm utilizes previous computations to update a generalized QR decomposition by a single row. The sparse structure of the model is exploited. Theoretical measures of computational complexity are provided. Experimental results confirm the ability of the new algorithms to identify outlying observations.
ISSN: 0167-9473
DOI: 10.1016/j.csda.2010.04.023
Rights: © Elsevier
Type: Article
Affiliation: Cyprus University of Technology 
Affiliation : Universität Basel 
University of Cyprus 
Queen Mary University of London 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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