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 
Keywords: Algorithms;Matrices;Computational complexity;Regression analysis
Category: Economics and Business
Field: Social Sciences
Issue Date: 2010
Publisher: Elsevier
Source: Computational Statistics and Data Analysis, 2010, Volume 54, Issue 12, Pages 3392-3403
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.
URI: http://ktisis.cut.ac.cy/handle/10488/6723
ISSN: 01679473
DOI: http://dx.doi.org/10.1016/j.csda.2010.04.023
Rights: © 2010 Elsevier B.V. All rights reserved.
Type: Article
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