Document details for 'Robust regression with compositional covariates including cellwise outliers'

Authors Štefelová, N., Alfons, A., Palarea Albaladejo, J., Filzmoser, P. and Hron, K.
Publication details Advances in Data Analysis and Classification. Springer.
Publisher details Springer
Keywords Cellwise outliers, Compositional data, Regression analysis
Abstract We propose a robust procedure to estimate a linear regression model with compo- sitional 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 composi- tional 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.
Last updated 2021-02-26

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