Use of biplot technique for the comparison of the missing value imputation methods

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dc.contributor.authorAlkan, B. Baris
dc.contributor.authorAlkan, Nesrin
dc.contributor.authorAtakan, Cemal
dc.contributor.authorTerzi, Yuksel
dc.date.accessioned2025-03-23T19:16:52Z
dc.date.available2025-03-23T19:16:52Z
dc.date.issued2015
dc.departmentSinop Üniversitesi
dc.description.abstractThis study was performed to assess the effects of different imputation methods on the performance of a biplot technique. We selected the Fisher's iris data as our reference dataset. Some elements of the Iris data were deleted in different rates under missing at random (MAR) assumption to generate incomplete datasets which had 3.5%, 7%, %15, 20% missing value. Datasets with missing values were completed by four imputation methods [mean imputation, regression imputation, expectation maximisation (EM) algorithm, multiple imputation (MI)]. The new imputed datasets were analysed by biplot technique and their results were compared with original complete biplot of the data. The results of biplot analysis were similar in all the imputation methods when missing rate is low under MAR assumption. Even when the missing rate was greater than 10%, results of EM and MI methods were similar to real values and graphical representation of original data. For multivariate methods, we also propose filling in the missing value with the arithmetic mean of the imputed estimates which are obtained with multiple imputation. This paper also indicates that the use of biplot technique for the comparison of the missing value imputation methods provides a useful visual tool. © 2015 Inderscience Enterprises Ltd.
dc.identifier.doi10.1504/IJDATS.2015.071367
dc.identifier.endpage230
dc.identifier.issn1755-8050
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84996561684
dc.identifier.scopusqualityQ3
dc.identifier.startpage217
dc.identifier.urihttps://doi.org/10.1504/IJDATS.2015.071367
dc.identifier.urihttps://hdl.handle.net/11486/4203
dc.identifier.volume7
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInderscience Enterprises Ltd.
dc.relation.ispartofInternational Journal of Data Analysis Techniques and Strategies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250323
dc.subjectBiplot
dc.subjectImputation methods
dc.subjectMissing value
dc.titleUse of biplot technique for the comparison of the missing value imputation methods
dc.typeArticle

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