Trimmed LASSO regression estimator for binary response data

DOI10.1016/J.SPL.2019.108679zbMath1436.62344OpenAlexW2994282567MaRDI QIDQ1987665

Yuehua Cui, Tong Wang, Qian Gao, Hongwei Sun

Publication date: 15 April 2020

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2019.108679

zbMATH Keywords

breakdown pointvariable selectionLassopenalized logistic regressionmaximum trimmed likelihood

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Generalized linear models (logistic models) (62J12)

Related Items (1)

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Uses Software

• SMOTE

Cites Work

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• SMOTE
• Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
• The breakdown behavior of the maximum likelihood estimator in the logistic regression model.
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator

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