Robust Regression and Outlier Detection

John Wiley & Sons, 2005 M02 25 - 329 páginas
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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


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1 Introduction
2 Simple Regression
3 Multiple Regression
4 The Special Case of OneDimensional Location
5 Algorithms
6 Outlier Diagnostics
7 Related Statistical Techniques
Table of Data Sets
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Página v - ... We can apply the rules primarily appropriate to observations obeying the typical Law of Error, to find both the most probable and the most advantageous Mean. And thus we find that the Arithmetic Mean is the best solution for a single cluster (and the Weighted Arithmetic Mean for several clusters) . The Method of Least Squares is seen to be our best course when we have thrown overboard a certain portion of our data — a sort of sacrifice which has often to be made by those who sail upon the stormy...

Acerca del autor (2005)

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.

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