Robust statistical procedures: Asymptotics and interrelations: Jana Jurečková and Pranab Kumar Sen, (Wiley, New York, 1996, Price: $59.95, ISBN No.: 0471 822213, xiv + 466 pp.)
✍ Scribed by Clint W. Coakley
- Book ID
- 104340475
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 176 KB
- Volume
- 64
- Category
- Article
- ISSN
- 0378-3758
No coin nor oath required. For personal study only.
✦ Synopsis
This book brings together a large body of results on estimators that are insensitive (globally or locally) to violations of the assumptions of the underlying statistical models. The subtitle holds the key to understanding the contents of this well written, mathematically sophisticated treatise, which provides an indepth treatment of the three most well known classes of robust statistical estimators and gives conditions under which they are asymptotically equivalent to one another. The material is organised in two parts with chapter divisions as follows:
Chapter 1. Introduction and Synopsis Part I. Asymptotics and Interrelations 2. Preliminaries 3. Robust Estimation of Location and Regression 4. Asymptotic Representations for L-Estimators 5. Asymptotic Representations for M-Estimators 6. Asymptotic Representations for R-Estimators 7. Asymptotic interrelations of Estimators Part II. Robust Statistical Inference 8. Robust Sequential and Recursive Point Estimation 9. Robust Confidence Sets and Intervals 10. Robust Statistical Tests
Chapter 1 contains a fair and accurate assessment of the contribution of the book, the level of presentation, and the requisite background needed for a satisfying reading experience, as well as an overview of the topics covered. On pp. 6-7 the authors state, "By making this book mathematically rigorous, theoretically sound, and statistically motivating, we have primarily aimed at the advanced graduate level, for coursework on asymptotic theory of robust statistical inference with special emphasis on the interrelationships among families of (competing) statistics .... The reader is of course expected to be familiar with the basic theory of statistical inference and decision theory including estimation theory, hypothesis testing, and classical linear models, at least, at an intermediate level, though a measuretheoretic orientation is not that essential. Advanced calculus, real analysis,