Details

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

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

<b>The Wiley-Interscience Paperback Series</b> consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. <p>"This is a nice book containing a wealth of information, much of it due to the authors. . . . If an instructor designing such a course wanted a textbook, this book would be the best choice available. . . . There are many stimulating exercises, and the book also contains an excellent index and an extensive list of references."<br /> —<b><i>Technometrics</i></b></p> <p>"[This] book should be read carefully by anyone who is interested in dealing with statistical models in a realistic fashion."<br /> —<b><i>American Scientist</i></b></p> <p>Introducing concepts, theory, and applications, <i>Robust Statistics</i> 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.</p>

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