Robust mislabel logistic regression without modeling mislabel probabilities

DOI10.1111/biom.12726zbMath1415.62107arXiv1608.04503OpenAlexW2963388466WikidataQ38792383 ScholiaQ38792383MaRDI QIDQ3119819

Su-Yun Huang, Hung Hung, Zhi-Yu Jou

Publication date: 13 March 2019

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.04503

zbMATH Keywords

classificationlogistic regressionminimum divergence estimationmislabeled responserobust \(M\)-estimation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (4)

Robust polytomous logistic regression ⋮ Gene–environment interaction identification via penalized robust divergence ⋮ Robust regression against heavy heterogeneous contamination ⋮ Robust high-dimensional regression for data with anomalous responses

Cites Work

• Robust parameter estimation with a small bias against heavy contamination
• A boosting method with asymmetric mislabeling probabilities which depend on covariates
• Robust estimation in generalized linear models: the density power divergence approach
• A comparison of related density-based minimum divergence estimators
• Robust estimation under heavy contamination using unnormalized models
• Logistic discrimination using robust estimators: An influence function approach
• Conditionally Unbiased Bounded-Influence Estimation in General Regression Models, with Applications to Generalized Linear Models
• Robustifying AdaBoost by Adding the Naive Error Rate
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