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dc.contributor.authorDeliorman, Gokce
dc.contributor.authorInan, D.
dc.date.accessioned2021-03-15T13:06:10Z
dc.date.available2021-03-15T13:06:10Z
dc.date.issued2020
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.urihttps://doi.org/10.1080/02664763.2020.1779196
dc.identifier.urihttps://hdl.handle.net/20.500.12879/109
dc.descriptionInan, Deniz/0000-0002-0408-1309en_US
dc.descriptionWOS:000545818100001en_US
dc.description.abstractAn influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.en_US
dc.description.sponsorshipMarmara University (Scientific Research Project Unit)Marmara University [FEN-C-YLP-170419-0131]en_US
dc.description.sponsorshipThe authors were supported by the Marmara University (Scientific Research Project Unit, Project Number: FEN-C-YLP-170419-0131).en_US
dc.language.isoengen_US
dc.publisherTaylor & Francis Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInfluential subsetsen_US
dc.subjectbinary particle swarm optimizationen_US
dc.subjectheuristic algorithmsen_US
dc.subjectlinear regressionen_US
dc.subjectdiagnosticsen_US
dc.titleBinary particle swarm optimization as a detection tool for influential subsets in linear regressionen_US
dc.typearticleen_US
dc.contributor.departmentBeykoz Üniversitesi Muhendislik ve Mimarlik Fakultesien_US
dc.contributor.institutionauthorDeliorman, Gokce
dc.identifier.doi10.1080/02664763.2020.1779196
dc.relation.journalJournal Of Applied Statisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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