Title: Imputation of rounded zeros for high-dimensional compositional data
Authors : Templ, Matthias
Hron, Karel
Filzmoser, Peter
Gardlo, Alžbӗta
Published in : Chemometrics and Intelligent Laboratory Systems
Volume(Issue) : 155
Pages : 183
Pages to: 190
Publisher / Ed. Institution : Elsevier
Issue Date: 2016
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subject (DDC) : 500: Natural sciences and mathematics
Abstract: High-dimensional compositional data, multivariate observations carrying relative information, frequently contain values below a detection limit (rounded zeros). We introduce new model-based procedures for replacing these values with reasonable numbers, so that the completed data set is ready for use with statistical analysis methods that rely on complete data, such as regression or classification with high-dimensional explanatory variables. The procedures respect the geometry of compositional data and can be considered as alternatives to existing methods. Simulations show that especially in high-dimensions, the proposed methods outperform existing methods. Moreover, even for a large number of rounded zeros, the new methods lead to an improved quality of the data, which is important for further analyses. The usefulness of the procedure is demonstrated using a data example from metabolomics.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific Journal
DOI : 10.1016/j.chemolab.2016.04.011
ISSN: 01697439
URI: https://digitalcollection.zhaw.ch/handle/11475/5691
Appears in Collections:Publikationen School of Engineering

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