Robust statistical procedures / Peter J. Huber.

Format:

Book

Author/Creator:

Huber, Peter J.

Contributor:

Society for Industrial and Applied Mathematics.

Series:

CBMS-NSF regional conference series in applied mathematics ; 68.

CBMS-NSF regional conference series in applied mathematics ; 68

Language:

English

Subjects (All):

Robust statistics.

Distribution (Probability theory).

Physical Description:

1 electronic text (ix, 67 p.) : ill., digital file.

Edition:

2nd ed.

Place of Publication:

Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 1996.

Language Note:

English

System Details:

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Summary:

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.

Contents:

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.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references (p. 65-67).

Title from title screen, viewed 04/05/2011.

ISBN:

1-61197-003-2

Publisher Number:

CB68 SIAM