Robust logistic zero-sum regression for microbiome compositional data

Autor:	Peter Filzmoser, G Monti
Přispěvatelé:	Monti, G, Filzmoser, P
Rok vydání:	2021
Předmět:	Statistics and Probability High dimensional data Applied Mathematics Estimator Feature selection Logistic regression Regression Computer Science Applications Metagenomic Robustness (computer science) Penalized estimation Statistics Outlier Covariate Robustne Compositional data Mathematics
Zdroj:	Advances in Data Analysis and Classification. 16:301-324
ISSN:	1862-5355 1862-5347
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2cf54888594025ef7180d6e0fc3632 https://doi.org/10.1007/s11634-021-00465-4 Zobrazit plný text záznamu Full text from SpringerLink