Please use this identifier to cite or link to this item:
|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|
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.