Robust Statistical Procedures
2nd Edition
Will be published approx. on 31. December 1996
Book
Paperback/Softback
77 pages
978-0-89871-379-4 (ISBN)
Description
Here is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition.
More details
Series
Edition
Second Edition
Language
English
Place of publication
New York
United States
Target group
College/higher education
Edition type
New edition
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 252 mm
Width: 172 mm
Thickness: 8 mm
Weight
154 gr
ISBN-13
978-0-89871-379-4 (9780898713794)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Peter J. Huber
Robust Statistical Procedures
Book
11/1977
Society for Industrial & Applied Mathematics,U.S.
€40.43
Article exhausted; check for reprint
Content
Preface to the Second Edition
Preface to the First Edition
Chapter 1: Background. Why robust procedures?
Chapter 2: Qualitative and Quantitative Robustness. Qualitative robustness
Quantitative robustness, breakdown
Infinitesimal robustness, influence function
Chapter 3: M-,L-, and R-Estimates. M-estimates
L-estimates
R-estimates
Asymptotic properties of M-estimates
Asymptotically efficient M-, L-, R-estimates
Scaling question
Chapter 4: Asymptotic Minimax Theory. Minimax asymptotic bias
Minimax asymptotic variance
Chapter 5: Multiparameter Problems. Generalities
Regression
Robust covariances: the affinely invariant case
Robust covariances: the coordinate dependent case
Chapter 6: Finite Sample Minimax Theory. Robust tests and capacities
Finite sample minimax estimation
Chapter 7: Adaptive Estimates. Adaptive estimates
Chapter 8: Robustness: Where are We Now? The first ten years
Influence functions and psuedovalues
Breakdown and outlier detection
Studentizing
Shrinking neighborhoods
Design
Regression
Multivariate problems
Some persistent misunderstandings
Future directions
References.
Preface to the First Edition
Chapter 1: Background. Why robust procedures?
Chapter 2: Qualitative and Quantitative Robustness. Qualitative robustness
Quantitative robustness, breakdown
Infinitesimal robustness, influence function
Chapter 3: M-,L-, and R-Estimates. M-estimates
L-estimates
R-estimates
Asymptotic properties of M-estimates
Asymptotically efficient M-, L-, R-estimates
Scaling question
Chapter 4: Asymptotic Minimax Theory. Minimax asymptotic bias
Minimax asymptotic variance
Chapter 5: Multiparameter Problems. Generalities
Regression
Robust covariances: the affinely invariant case
Robust covariances: the coordinate dependent case
Chapter 6: Finite Sample Minimax Theory. Robust tests and capacities
Finite sample minimax estimation
Chapter 7: Adaptive Estimates. Adaptive estimates
Chapter 8: Robustness: Where are We Now? The first ten years
Influence functions and psuedovalues
Breakdown and outlier detection
Studentizing
Shrinking neighborhoods
Design
Regression
Multivariate problems
Some persistent misunderstandings
Future directions
References.