A comparative analysis of multiple outlier detection procedures in the linear regression model.

DOI10.1016/S0167-9473(00)00042-6zbMath1038.62062OpenAlexW1986320187MaRDI QIDQ5941110

James R. Simpson, Douglas C. Montgomery, James W. Wisnowski

Publication date: 20 August 2001

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(00)00042-6

zbMATH Keywords

Monte Carlo simulationMultiple outliersMinimum volume ellipsoidOutlierRobust regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Monte Carlo methods (65C05) Diagnostics, and linear inference and regression (62J20)

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Uses Software

• ROBETH

Cites Work

• High breakdown-point and high efficiency robust estimates for regression
• A clustering algorithm for identifying multiple outliers in linear regression.
• Robust regression: Asymptotics, conjectures and Monte Carlo
• A Bounded Influence, High Breakdown, Efficient Regression Estimator
• Least Median of Squares Regression
• Using Robust Scale Estimates in Detecting Multiple Outliers in Linear Regression
• Identification of Outliers in Multivariate Data
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