Robust regression with compositional covariates including cellwise outliers

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610085" target="_blank" >RIV/61989592:15310/21:73610085 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11634-021-00436-9" target="_blank" >https://link.springer.com/article/10.1007/s11634-021-00436-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11634-021-00436-9" target="_blank" >10.1007/s11634-021-00436-9</a>

Result language
angličtina
Original language name
Robust regression with compositional covariates including cellwise outliers
Original language description
We propose a robust procedure to estimate a linear regression model with compositional and real-valued explanatory variables. The proposed procedure is designed to be robust against individual outlying cells in the data matrix (cellwise outliers), as well as entire outlying observations (rowwise outliers). Cellwise outliers are first filtered and then imputed by robust estimates. Afterwards, rowwise robust compositional regression is performed to obtain model coefficient estimates. Simulations show that the procedure generally outperforms a traditional rowwise-only robust regression method (MM-estimator). Moreover, our procedure yields better or comparable results to recently proposed cellwise robust regression methods (shooting S-estimator, 3-step regression) while it is preferable for interpretation through the use of appropriate coordinate systems for compositional data. An application to bio-environmental data reveals that the proposed procedure—compared to other regression methods—leads to conclusions that are best aligned with established scientific knowledge.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GA19-07155S" target="_blank" >GA19-07155S: Identification of regulatory networks controlling pea seed coat development using combination of RNA sequencing, protein and metabolites analysis.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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