E-Book, Englisch, 330 Seiten, E-Book
Rousseeuw / Leroy Robust Regression and Outlier Detection
1. Auflage 2005
ISBN: 978-0-471-72537-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 330 Seiten, E-Book
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-0-471-72537-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists of selectedbooks that have been made more accessible to consumers in an effortto increase global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists.
"The writing style is clear and informal, and much of thediscussion is oriented to application. In short, the book is akeeper."
-Mathematical Geology
"I would highly recommend the addition of this book to thelibraries of both students and professionals. It is a usefultextbook for the graduate student, because it emphasizes both thephilosophy and practice of robustness in regression settings, andit provides excellent examples of precise, logical proofs oftheorems. . . .Even for those who are familiar with robustness, thebook will be a good reference because it consolidates the researchin high-breakdown affine equivariant estimators and includes anextensive 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 statisticalpractice.' Rousseeuw and Leroy have included all of thenecessary ingredients to make this happen."
-Journal of the American Statistical Association
Autoren/Hrsg.
Weitere Infos & Material
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.




