Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2055
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dc.contributor.authorHENS, Niel-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorTHIJS, Herbert-
dc.contributor.authorVERBEKE, Geert-
dc.date.accessioned2007-11-11T09:38:10Z-
dc.date.available2007-11-11T09:38:10Z-
dc.date.issued2005-
dc.identifier.citationCOMPUTATIONAL STATISTICS & DATA ANALYSIS, 48(3). p. 467-487-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/1942/2055-
dc.description.abstractTo asses the sensitivity of conclusions to model choices in the context of selection models for non-random dropout, several methods have been developed. None of them are without limitations. A new method called kernel weighted influence is proposed. While global and local influence approaches look upon the influence of cases, this new method looks at the influence of types of observations. The basic idea is to combine the existing influence approaches with a non-parametric weighting scheme. The kernel weighted global influence offers a possible solution to the problem of masking, while the kernel weighted local influence can be seen as a tool to better understand the source of influence. (C) 2004 Elsevier B.V. All rights reserved.-
dc.description.sponsorshipWe gratefully acknowledge support form the Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data”.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.rights(c) 2004 Elsevier B.V. All rights reserved-
dc.subject.otherlocal influence; global influence; Kernel weights; missing data; sensitivity analysis; weighted likelihood-
dc.subject.otherlocal influence; global influence; kernel weights; missing data; sensitivity analysis; weighted; likelihood-
dc.titleKernel weighted influence measures-
dc.typeJournal Contribution-
dc.identifier.epage487-
dc.identifier.issue3-
dc.identifier.spage467-
dc.identifier.volume48-
local.format.pages21-
local.bibliographicCitation.jcatA1-
dc.description.notesLimburgs Univ Ctr, Ctr Stat, B-3590 Diepenbeek, Belgium. Katholieke Univ Leuven, Ctr Biostat, B-3000 Louvain, Belgium.Hens, N, Limburgs Univ Ctr, Ctr Stat, Univ Campus,Bldg D, B-3590 Diepenbeek, Belgium.niel.hens@luc.ac.be-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1016/j.csda.2004.02.010-
dc.identifier.isi000226475800003-
item.validationecoom 2006-
item.accessRightsOpen Access-
item.fullcitationHENS, Niel; AERTS, Marc; MOLENBERGHS, Geert; THIJS, Herbert & VERBEKE, Geert (2005) Kernel weighted influence measures. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 48(3). p. 467-487.-
item.fulltextWith Fulltext-
item.contributorHENS, Niel-
item.contributorAERTS, Marc-
item.contributorMOLENBERGHS, Geert-
item.contributorTHIJS, Herbert-
item.contributorVERBEKE, Geert-
crisitem.journal.issn0167-9473-
crisitem.journal.eissn1872-7352-
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