Using Wald-type estimator to combat outliers and Berkson-type uncertainties with mixture distributions in linear regression models

DOI10.1080/03610926.2017.1353627OpenAlexW2738036730MaRDI QIDQ5160258

Li-Hsueh Cheng, Yuh-Jenn Wu, Wei-Quan Fang

Publication date: 28 October 2021

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2017.1353627

zbMATH Keywords

outlierslinear regressionWald-type estimatorBerkson-type uncertainties

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

Sparse linear regression models of high dimensional covariates with non-Gaussian outliers and Berkson error-in-variable under heteroscedasticity

Uses Software

• robustbase
• FSDA
• KEGG

Cites Work

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• High breakdown-point and high efficiency robust estimates for regression
• Reweighted LS estimators converge at the same rate as the initial estimator
• On the Asymptotic Bias of Wald-Type Estimators of a Straight Line when Both Variables are Subject to Error
• Using Liu-Type Estimator to Combat Collinearity
• One-stepM-estimators: Jones and Faddy's skewedt-distribution
• Consistent estimation approach to tackling collinearity and Berkson-type measurement error in linear regression using adjusted Wald-type estimator
• Robust Statistics
• Robust Estimation of a Location Parameter
• Are There Two Regressions?

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