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Introducing concepts, theory, and applications, Robust Statistics is accessible to a broad audience, avoiding allusions to high--powered mathematics while emphasizing ideas, heuristics, and background. The text covers the approach based on the influence function (the effect of an outlier on an estimater, for example) and related notions such as the breakdown point. It also treats the change--of--variance function, fundamental concepts and results in the framework of estimation of a single parameter, and applications to estimation of covariance matrices and regression parameters. Robust Statistics is a leading--edge resource suitable for use both as a textbook or a reference on robust statistics for all practitioners and students.
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Table of Contents

1. Introduction and Motivation. 2. One-Dimensional Estimators. 3. One-Dimensional Tests. 4. Multidimensional Estimators. 5. Estimation of Covariance Matrices and Multivariate Location. 6. Linear Models: Robust Estimation. 7. Linear Models: Robust Testing. 8. Complements and Outlook. References. Index.

About the Author

FRANK R. HAMPEL, PhD, is Professor of Statistics in the Department of Mathematics at the Swiss Federal Institute of Technology (ETH) Zurich, Switzerland. ELVEZIO M. RONCHETTI, PhD, is Professor of Statistics in the Department of Econometrics at the University of Geneva in Switzerland. PETER J. ROUSSEEUW, PhD, is Professor in the Department of Mathematics and Computer Science at the University of Antwerp in Belgium. WERNER A. STAHEL, PhD, is Professor at the Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.

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