Robust Regression and Outlier Detection

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. Exposition focuses on the least median of squares technique, which is intuitive and easy to use, and many real-data examples are given. Chapter coverage includes robust multiple regression, the special case of one-dimensional location, algorithms, outlier diagnostics, and robustness in related fields, such as the estimation of multivariate location and covariance matrices, and time series analysis.

Peter J. Rousseeuw, PhD, is currently a Professor at the University of Antwerp in Belgium. He received his PhD in Statistics in 1981. His research interests include the influence function approach to robust statistics and cluster analysis.

Annick M. Leroy is affiliated with Vrije University in Brussels, Belgium.

1. Introduction.

2. Simple Regression.

3. Multiple Regression.

4. The Special Case of One-Dimensional Location.

5. Algorithms.

6. Outlier Diagnostics.

7. Related Statistical Techniques.

References.

Table of Data Sets.

Index.

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Robust Regression and Outlier Detection

Summary

Author Biography

Table of Contents

Supplemental Materials