Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://hdl.handle.net/20.500.14279/1994
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
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.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 0167-9473 | - |
crisitem.journal.publisher | Elsevier | - |
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 | - |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
3
checked on 9 Νοε 2023
WEB OF SCIENCETM
Citations
50
4
Last Week
0
0
Last month
0
0
checked on 29 Οκτ 2023
Page view(s)
546
Last Week
0
0
Last month
32
32
checked on 13 Μαρ 2025
Google ScholarTM
Check
Altmetric
Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα