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.

Monti, G., Filzmoser, P. (2022). Robust logistic zero-sum regression for microbiome compositional data. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 16(2), 301-324 [10.1007/s11634-021-00465-4].

Robust logistic zero-sum regression for microbiome compositional data

Monti, GS
;
2022

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.
Articolo in rivista - Articolo scientifico
High dimensional data; Metagenomics; Penalized estimation; Robustness;
English
30-set-2021
2022
16
2
301
324
none
Monti, G., Filzmoser, P. (2022). Robust logistic zero-sum regression for microbiome compositional data. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 16(2), 301-324 [10.1007/s11634-021-00465-4].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/329316
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