Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9115
DC FieldValueLanguage
dc.contributor.authorHadjiantoni, Stella-
dc.contributor.authorKontoghiorghes, Erricos John-
dc.date.accessioned2017-01-18T14:52:26Z-
dc.date.available2017-01-18T14:52:26Z-
dc.date.issued2017-03-01-
dc.identifier.citationStatistics and Computing, 2017, vol. 27, no. 2, pp. 349–361en_US
dc.identifier.issn15731375-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/9115-
dc.description.abstractA new numerical method to solve the downdating problem (and variants thereof), namely removing the effect of some observations from the generalized least squares (GLS) estimator of the general linear model (GLM) after it has been estimated, is extensively investigated. It is verified that the solution of the downdated least squares problem can be obtained from the estimation of an equivalent GLM, where the original model is updated with the imaginary deleted observations. This updated GLM has a non positive definite dispersion matrix which comprises complex covariance values and it is proved herein to yield the same normal equations as the downdated model. Additionally, the problem of deleting observations from the seemingly unrelated regressions model is addressed, demonstrating the direct applicability of this method to other multivariate linear models. The algorithms which implement the novel downdating method utilize efficiently the previous computations from the estimation of the original model. As a result, the computational cost is significantly reduced. This shows the great usability potential of the downdating method in computationally intensive problems. The downdating algorithms have been applied to real and synthetic data to illustrate their efficiency.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofStatistics and Computingen_US
dc.rights© Springeren_US
dc.subjectDowndatingen_US
dc.subjectGeneralized least squaresen_US
dc.subjectSeemingly unrelated regressionsen_US
dc.subjectSingular dispersion matrixen_US
dc.subjectUpdatingen_US
dc.titleEstimating large-scale general linear and seemingly unrelated regressions models after deleting observationsen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationQueen Mary University of Londonen_US
dc.collaborationUniversity of Londonen_US
dc.subject.categoryEconomics and Businessen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1007/s11222-016-9626-5en_US
dc.relation.issue2en_US
dc.relation.volume27en_US
cut.common.academicyear2016-2017en_US
dc.identifier.spage349en_US
dc.identifier.epage361en_US
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.journal.journalissn1573-1375-
crisitem.journal.publisherSpringer Nature-
crisitem.author.deptDepartment of Finance, Accounting and Management Science-
crisitem.author.facultyFaculty of Tourism Management, Hospitality and Entrepreneurship-
crisitem.author.orcid0000-0001-9704-9510-
crisitem.author.parentorgFaculty of Management and Economics-
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