Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

Chen, Jiajia; Zhang, Xiaoqin; Hron, Karel; Templ, Matthias; Li, Shengjia

doi:10.1080/02664763.2017.1410524

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data
Authors:	Chen, Jiajia Zhang, Xiaoqin Hron, Karel Templ, Matthias Li, Shengjia
DOI:	10.1080/02664763.2017.1410524
Published in:	Journal of Applied Statistics
Volume(Issue):	45
Issue:	11
Page(s):	2067
Pages to:	2080
Issue Date:	2018
Publisher / Ed. Institution:	Taylor & Francis
ISSN:	0266-4763 1360-0532
Language:	English
Subjects:	Compositional data; Centered logratio coordinates; Rounded zeros; Cluster analysis; Partial least squares regression
Subject (DDC):	510: Mathematics
Abstract:	The 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/12761
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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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.