dc.creatorLabra, FV
dc.creatorAoki, R
dc.creatorBolfarine, H
dc.date2005
dc.dateSEP
dc.date2014-07-30T13:38:42Z
dc.date2015-11-26T16:33:30Z
dc.date2014-07-30T13:38:42Z
dc.date2015-11-26T16:33:30Z
dc.date.accessioned2018-03-28T23:15:22Z
dc.date.available2018-03-28T23:15:22Z
dc.identifierJournal Of Applied Statistics. Routledge Journals, Taylor & Francis Ltd, v. 32, n. 7, n. 723, n. 739, 2005.
dc.identifier0266-4763
dc.identifierWOS:000232466700005
dc.identifier10.1080/02664760500079639
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/52524
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/52524
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1270869
dc.descriptionIn this paper we discuss the application of local influence in a measurement error regression model with null intercepts under a Student_t model with dependent populations. The Student_t distribution is a robust alternative to modelling data sets involving errors with longer than Normal tails. We derive the appropriate matrices for assessing the local influence for different perturbation schemes and use real data as an illustration of the usefulness of the application.
dc.description32
dc.description7
dc.description723
dc.description739
dc.languageen
dc.publisherRoutledge Journals, Taylor & Francis Ltd
dc.publisherAbingdon
dc.publisherInglaterra
dc.relationJournal Of Applied Statistics
dc.relationJ. Appl. Stat.
dc.rightsfechado
dc.rightshttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dc.sourceWeb of Science
dc.subjectinfluence diagnostic
dc.subjectStudent_t model
dc.subjectlikelihood displacement
dc.subjectpretest/post-test data
dc.subjectmeasurement error models
dc.subjectIn-variables Model
dc.subjectMaximum-likelihood
dc.subjectDiagnostics
dc.titleLocal influence in null intercept measurement error regression under a Student_t model
dc.typeArtículos de revistas


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