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dc.contributor.authorChen, Jiajia-
dc.contributor.authorZhang, Xiaoqin-
dc.contributor.authorHron, Karel-
dc.contributor.authorTempl, Matthias-
dc.contributor.authorLi, Shengjia-
dc.date.accessioned2018-11-13T15:42:44Z-
dc.date.available2018-11-13T15:42:44Z-
dc.date.issued2018-
dc.identifier.issn0266-4763de_CH
dc.identifier.issn1360-0532de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/12761-
dc.description.abstractThe logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates. We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.de_CH
dc.language.isoende_CH
dc.publisherTaylor & Francisde_CH
dc.relation.ispartofJournal of Applied Statisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCompositional datade_CH
dc.subjectCentered logratio coordinatesde_CH
dc.subjectRounded zerosde_CH
dc.subjectCluster analysisde_CH
dc.subjectPartial least squares regressionde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleRegression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional datade_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1080/02664763.2017.1410524de_CH
zhaw.funding.euNode_CH
zhaw.issue11de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end2080de_CH
zhaw.pages.start2067de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume45de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Chen, J., Zhang, X., Hron, K., Templ, M., & Li, S. (2018). Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data. Journal of Applied Statistics, 45(11), 2067–2080. https://doi.org/10.1080/02664763.2017.1410524
Chen, J. et al. (2018) ‘Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data’, Journal of Applied Statistics, 45(11), pp. 2067–2080. Available at: https://doi.org/10.1080/02664763.2017.1410524.
J. Chen, X. Zhang, K. Hron, M. Templ, and S. Li, “Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data,” Journal of Applied Statistics, vol. 45, no. 11, pp. 2067–2080, 2018, doi: 10.1080/02664763.2017.1410524.
CHEN, Jiajia, Xiaoqin ZHANG, Karel HRON, Matthias TEMPL und Shengjia LI, 2018. Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data. Journal of Applied Statistics. 2018. Bd. 45, Nr. 11, S. 2067–2080. DOI 10.1080/02664763.2017.1410524
Chen, Jiajia, Xiaoqin Zhang, Karel Hron, Matthias Templ, and Shengjia Li. 2018. “Regression Imputation with Q-Mode Clustering for Rounded Zero Replacement in High-Dimensional Compositional Data.” Journal of Applied Statistics 45 (11): 2067–80. https://doi.org/10.1080/02664763.2017.1410524.
Chen, Jiajia, et al. “Regression Imputation with Q-Mode Clustering for Rounded Zero Replacement in High-Dimensional Compositional Data.” Journal of Applied Statistics, vol. 45, no. 11, 2018, pp. 2067–80, https://doi.org/10.1080/02664763.2017.1410524.


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