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D�tails bibliographiques

Titre: Robust Regression and Outlier Detection
�diteur: John Wiley & Sons Inc
Date de parution: 1987
Langue: anglais
ISBN � 10 chiffres: 0471852333
ISBN � 13 chiffres: 9780471852339
Reliure: Hardcover
�tat de l'article: Tr�s bon

A propos de ce titre

Synopsis

Provides an applications-oriented introduction to robust regression and outlier detection, emphasising -high-breakdown- methods which can cope with a sizeable fraction of contamination. Its self-contained treatment allows readers to skip the mathematical material which is concentrated in a few sections. Exposition focuses on the least median of squares technique, which is intuitive and easy to use, and many real-data examples are given. Chapter coverage includes robust multiple regression, the special case of one-dimensional location, algorithms, outlier diagnostics, and robustness in related fields, such as the estimation of multivariate location and covariance matrices, and time series analysis.

Quatri�me de couverture

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

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