
Robust Statistics
Peter J. Huber(Author)
Wiley (Publisher)
1st Edition
Published on 23. December 2003
Book
Paperback/Softback
328 pages
978-0-471-65072-0 (ISBN)
Article exhausted; check for reprint
Description
The traditional setting of statistical inference involves finite or known spaces and subjects. During the past few decades, a theory has been developed that allows the sample space to be abstract or unknown. More recently, mathematical techniques-especially the method of sieves-have been constructed to enable inferences to be made in abstract parameter spaces. This work began with the author's 1950 monograph on inference in stochastic processes (for general sample space) and with the sieve methodology (for general parameter space) This paperback reprint of a Wiley bestseller studies both of these cases and represents the first comprehensive treatment of the subject.
Reviews / Votes
"...informative and worth the effort for anyone with an interest in this field..." (ISCB News 38, December 2004)More details
Series
Edition
1., Auflage
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 23.2 cm
Width: 15.4 cm
Thickness: 13 mm
Weight
369 gr
ISBN-13
978-0-471-65072-0 (9780471650720)
Schweitzer Classification
Other editions
New editions

Peter J. Huber | Elvezio M. Ronchetti
Robust Statistics
Book
03/2009
2nd Edition
Wiley
€155.00
Shipment within 10-20 days
Additional editions

Person
Peter J. Huber was formerly a Professor of Statistics at Harvard University and ETH Zurich. Dr. Huber received his Ph.D. in Mathematics from ETH Zurich in 1961.
Content
1. Generalities.
2. The Weak Topology and Its Metrization.
3. The Basic Types of Estimates.
4. Asymptotic Minimax Theory for Estimating a Location Parameter.
5. Scale Estimates.
6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale.
7. Regression.
8. Robust Covariance and Correlation Matrices.
9. Rubustness of Design.
10. Exact Finite Sample Results.
11. Miscellaneous Topics.
References.
Index.
2. The Weak Topology and Its Metrization.
3. The Basic Types of Estimates.
4. Asymptotic Minimax Theory for Estimating a Location Parameter.
5. Scale Estimates.
6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale.
7. Regression.
8. Robust Covariance and Correlation Matrices.
9. Rubustness of Design.
10. Exact Finite Sample Results.
11. Miscellaneous Topics.
References.
Index.