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https://doi.org/10.21256/zhaw-21934| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Comparison of zero replacement strategies for compositional data with large numbers of zeros |
| Autor/-in: | Lubbe, Sugnet Filzmoser, Peter Templ, Matthias |
| et. al: | No |
| DOI: | 10.1016/j.chemolab.2021.104248 10.21256/zhaw-21934 |
| Erschienen in: | Chemometrics and Intelligent Laboratory Systems |
| Band(Heft): | 210 |
| Heft: | 104248 |
| Erscheinungsdatum: | 2021 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 0169-7439 1873-3239 |
| Sprache: | Englisch |
| Schlagwörter: | Imputation; Compositional data analysis; Zero sum regression; Microbiome data |
| Fachgebiet (DDC): | 510: Mathematik |
| Zusammenfassung: | 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 (Templ, 2020) [1] 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/21934 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Lubbe_etal_Comparison-of-zero-replacement-strategies.pdf | 4.11 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210(104248). https://doi.org/10.1016/j.chemolab.2021.104248
Lubbe, S., Filzmoser, P. and Templ, M. (2021) ‘Comparison of zero replacement strategies for compositional data with large numbers of zeros’, Chemometrics and Intelligent Laboratory Systems, 210(104248). Available at: https://doi.org/10.1016/j.chemolab.2021.104248.
S. Lubbe, P. Filzmoser, and M. Templ, “Comparison of zero replacement strategies for compositional data with large numbers of zeros,” Chemometrics and Intelligent Laboratory Systems, vol. 210, no. 104248, 2021, doi: 10.1016/j.chemolab.2021.104248.
LUBBE, Sugnet, Peter FILZMOSER und Matthias TEMPL, 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems. 2021. Bd. 210, Nr. 104248. DOI 10.1016/j.chemolab.2021.104248
Lubbe, Sugnet, Peter Filzmoser, and Matthias Templ. 2021. “Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros.” Chemometrics and Intelligent Laboratory Systems 210 (104248). https://doi.org/10.1016/j.chemolab.2021.104248.
Lubbe, Sugnet, et al. “Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros.” Chemometrics and Intelligent Laboratory Systems, vol. 210, no. 104248, 2021, https://doi.org/10.1016/j.chemolab.2021.104248.
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