|Publication type:||Conference other|
|Type of review:||Peer review (abstract)|
|Title:||Strategies to replace high proportions of zeros in compositional data|
|Conference details:||1st Conference on Information Technology and Data Science, CITDS, Debrecen, Hungary / Online, 6-8 November 2020|
|Subjects:||Compositional data; Biomic data; High-dimensional data; Rounded zeros; Imputation; Replacement|
|Subject (DDC):||005: Computer programming, programs and data|
|Abstract:||Modern applications in chemometrics and bioinformatics result in compositional data sets with a high proportion of zeros. An example are microbiome data, where zeros refer to measurements below the detection limit of one count. When building statistical models, it is important that zeros are replaced by sensible values. Different replacement techniques from compositional data analysis are considered and compared by a simulation study and examples. The comparison also includes a recently proposed method  based on deep learning. Detailed insights into the appropriateness of the methods for a problem at hand are provided, and differences in the outcomes of statistical results are discussed.|
|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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