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G. Monti, P. Filzmoser:
"Robust logistic zero-sum regression for microbiome compositional data";
Advances in Data Analysis and Classification, 1 (2021).

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

http://dx.doi.org/10.1007/s11634-021-00465-4

https://link.springer.com/content/pdf/10.1007/s11634-021-00465-4.pdf