Robust logistic zero-sum regression for microbiome compositional data

G. S. Monti () and P. Filzmoser
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G. S. Monti: University of Milano-Bicocca
P. Filzmoser: Vienna University of Technology

Advances in Data Analysis and Classification, 2022, vol. 16, issue 2, No 4, 324 pages

Abstract: Abstract We introduce the Robust Logistic Zero-Sum Regression (RobLZS) estimator, which can be used for a two-class problem with high-dimensional compositional covariates. Since the log-contrast model is employed, the estimator is able to do feature selection among the compositional parts. The proposed method attains robustness by minimizing a trimmed sum of deviances. A comparison of the performance of the RobLZS estimator with a non-robust counterpart and with other sparse logistic regression estimators is conducted via Monte Carlo simulation studies. Two microbiome data applications are considered to investigate the stability of the estimators to the presence of outliers. Robust Logistic Zero-Sum Regression is available as an R package that can be downloaded at https://github.com/giannamonti/RobZS .

Keywords: Robustness; High dimensional data; Metagenomics; Penalized estimation; 62J07; 62F35; 62H30 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-021-00465-4

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