Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1994
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hofmann, Marc H. | - |
dc.contributor.author | Kontoghiorghes, Erricos John | - |
dc.date.accessioned | 2013-01-28T08:27:59Z | en |
dc.date.accessioned | 2013-05-16T08:22:22Z | - |
dc.date.accessioned | 2015-12-02T09:32:46Z | - |
dc.date.available | 2013-01-28T08:27:59Z | en |
dc.date.available | 2013-05-16T08:22:22Z | - |
dc.date.available | 2015-12-02T09:32:46Z | - |
dc.date.issued | 2010-12-01 | - |
dc.identifier.citation | Computational Statistics and Data Analysis, 2010, vol. 54, no. 12, pp. 3392-3403 | en_US |
dc.identifier.issn | 1679473 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1994 | - |
dc.description.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. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Computational Statistics and Data Analysis | en_US |
dc.rights | © Elsevier | en_US |
dc.subject | General linear model | en_US |
dc.subject | Generalized linear least squares | en_US |
dc.subject | Least trimmed squares | en_US |
dc.subject | Seemingly unrelated regressions | en_US |
dc.title | Matrix strategies for computing the least trimmed squares estimation of the general linear and sur models | en_US |
dc.type | Article | en_US |
dc.affiliation | Cyprus University of Technology | en |
dc.collaboration | Universität Basel | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | Queen Mary University of London | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Economics and Business | en_US |
dc.journals | Subscription | en_US |
dc.country | United Kingdom | en_US |
dc.country | Cyprus | en_US |
dc.country | Switzerland | en_US |
dc.subject.field | Social Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.csda.2010.04.023 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 12 | en_US |
dc.relation.volume | 54 | en_US |
cut.common.academicyear | 2010-2011 | en_US |
dc.identifier.spage | 3392 | en_US |
dc.identifier.epage | 3403 | en_US |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
crisitem.author.dept | Department of Finance, Accounting and Management Science | - |
crisitem.author.faculty | Faculty of Tourism Management, Hospitality and Entrepreneurship | - |
crisitem.author.orcid | 0000-0001-9704-9510 | - |
crisitem.author.parentorg | Faculty of Management and Economics | - |
crisitem.journal.journalissn | 0167-9473 | - |
crisitem.journal.publisher | Elsevier | - |
Appears in Collections: | Άρθρα/Articles |
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