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Robust Regression and Outlier Detection




Robust Regression and Outlier Detection

Peter J. Rousseeuw, Annick M. Leroy

ISBN: 978-0-471-48855-2 September 2003 329 Pages



The Wiley-Interscience Paperback Series 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.

"The writing style is clear and informal, and much of the discussion is oriented to application. In short, the book is a keeper."
–Mathematical Geology

"I would highly recommend the addition of this book to the libraries of both students and professionals. It is a useful textbook for the graduate student, because it emphasizes both the philosophy and practice of robustness in regression settings, and it provides excellent examples of precise, logical proofs of theorems. . . .Even for those who are familiar with robustness, the book will be a good reference because it consolidates the research in high-breakdown affine equivariant estimators and includes an extensive bibliography in robust regression, outlier diagnostics, and related methods. The aim of this book, the authors tell us, is ‘to make robust regression available for everyday statistical practice.’ Rousseeuw and Leroy have included all of the necessary ingredients to make this happen."
–Journal of the American Statistical Association

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.


Table of Data Sets. 


"…a wonderful book about methods of identifying outliers and then developing robust regression." (Journal of Statistical Computation and Simulation, July 2005)