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Robust regression and outlier detection

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Provides an applications-oriented introduction to robust regression and outlier detection, emphasising °high-breakdown° methods which can cope with a sizeable fraction of contamination. Its self-contained treatment allows readers to skip the mathematical material which is concentrated in a few sections.

Abstract: Provides an applications-oriented introduction to robust regression and outlier detection, emphasising °high-breakdown° methods which can cope with a sizeable fraction of contamination. Its self-contained treatment allows readers to skip the mathematical material which is concentrated in a few sections

Author(s): Rousseeuw, Peter J.; Leroy, Annick M
Publisher: John Wiley & Sons
Year: 1987

Language: English
Pages: 354
City: Hoboken
Tags: Least squares.;Outliers (Statistics);Regression analysis.;Mathematics.;Physical Sciences & Mathematics.;Mathematical Statistics.

Content: Robust Regression and Outlier Detection Contents 1. Introduction 2. Simple Regression 3. Multiple Regression 4. The Special Case of One-Dimensional Location 5. Algorithms 6. Outlier Diagnostics.